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  • 1.
    Bhat, Goutam
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Incept Inst Artificial Intelligence, U Arab Emirates.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Combining Local and Global Models for Robust Re-detection2018In: Proceedings of AVSS 2018. 2018 IEEE International Conference on Advanced Video and Signal-based Surveillance, Auckland, New Zealand, 27-30 November 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 25-30Conference paper (Refereed)
    Abstract [en]

    Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual tracking. However, these methods still struggle in occlusion and out-of-view scenarios due to the absence of a re-detection component. While such a component requires global knowledge of the scene to ensure robust re-detection of the target, the standard DCF is only trained on the local target neighborhood. In this paper, we augment the state-of-the-art DCF tracking framework with a re-detection component based on a global appearance model. First, we introduce a tracking confidence measure to detect target loss. Next, we propose a hard negative mining strategy to extract background distractors samples, used for training the global model. Finally, we propose a robust re-detection strategy that combines the global and local appearance model predictions. We perform comprehensive experiments on the challenging UAV123 and LTB35 datasets. Our approach shows consistent improvements over the baseline tracker, setting a new state-of-the-art on both datasets.

  • 2.
    Bhat, Goutam
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Unveiling the power of deep tracking2018In: Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part II / [ed] Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu and Yair Weiss, Cham: Springer Publishing Company, 2018, p. 493-509Conference paper (Refereed)
    Abstract [en]

    In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of >17% in EAO.

  • 3.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Learning Convolution Operators for Visual Tracking2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Visual tracking is one of the fundamental problems in computer vision. Its numerous applications include robotics, autonomous driving, augmented reality and 3D reconstruction. In essence, visual tracking can be described as the problem of estimating the trajectory of a target in a sequence of images. The target can be any image region or object of interest. While humans excel at this task, requiring little effort to perform accurate and robust visual tracking, it has proven difficult to automate. It has therefore remained one of the most active research topics in computer vision.

    In its most general form, no prior knowledge about the object of interest or environment is given, except for the initial target location. This general form of tracking is known as generic visual tracking. The unconstrained nature of this problem makes it particularly difficult, yet applicable to a wider range of scenarios. As no prior knowledge is given, the tracker must learn an appearance model of the target on-the-fly. Cast as a machine learning problem, it imposes several major challenges which are addressed in this thesis.

    The main purpose of this thesis is the study and advancement of the, so called, Discriminative Correlation Filter (DCF) framework, as it has shown to be particularly suitable for the tracking application. By utilizing properties of the Fourier transform, a correlation filter is discriminatively learned by efficiently minimizing a least-squares objective. The resulting filter is then applied to a new image in order to estimate the target location.

    This thesis contributes to the advancement of the DCF methodology in several aspects. The main contribution regards the learning of the appearance model: First, the problem of updating the appearance model with new training samples is covered. Efficient update rules and numerical solvers are investigated for this task. Second, the periodic assumption induced by the circular convolution in DCF is countered by proposing a spatial regularization component. Third, an adaptive model of the training set is proposed to alleviate the impact of corrupted or mislabeled training samples. Fourth, a continuous-space formulation of the DCF is introduced, enabling the fusion of multiresolution features and sub-pixel accurate predictions. Finally, the problems of computational complexity and overfitting are addressed by investigating dimensionality reduction techniques.

    As a second contribution, different feature representations for tracking are investigated. A particular focus is put on the analysis of color features, which had been largely overlooked in prior tracking research. This thesis also studies the use of deep features in DCF-based tracking. While many vision problems have greatly benefited from the advent of deep learning, it has proven difficult to harvest the power of such representations for tracking. In this thesis it is shown that both shallow and deep layers contribute positively. Furthermore, the problem of fusing their complementary properties is investigated.

    The final major contribution of this thesis regards the prediction of the target scale. In many applications, it is essential to track the scale, or size, of the target since it is strongly related to the relative distance. A thorough analysis of how to integrate scale estimation into the DCF framework is performed. A one-dimensional scale filter is proposed, enabling efficient and accurate scale estimation.

  • 4.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Gladh, Susanna
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Deep motion and appearance cues for visual tracking2019In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 124, p. 74-81Article in journal (Refereed)
    Abstract [en]

    Generic visual tracking is a challenging computer vision problem, with numerous applications. Most existing approaches rely on appearance information by employing either hand-crafted features or deep RGB features extracted from convolutional neural networks. Despite their success, these approaches struggle in case of ambiguous appearance information, leading to tracking failure. In such cases, we argue that motion cue provides discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. In this paper, we investigate the impact of deep motion features in a tracking-by-detection framework. We also evaluate the fusion of hand-crafted, deep RGB, and deep motion features and show that they contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly demonstrate that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.

  • 5.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    ECO: Efficient Convolution Operators for Tracking2017In: Proceedings 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 6931-6939Conference paper (Refereed)
    Abstract [en]

    In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. However, in the pursuit of ever increasing tracking performance, their characteristic speed and real-time capability have gradually faded. Further, the increasingly complex models, with massive number of trainable parameters, have introduced the risk of severe over-fitting. In this work, we tackle the key causes behind the problems of computational complexity and over-fitting, with the aim of simultaneously improving both speed and performance. We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model update strategy with improved robustness and reduced complexity. We perform comprehensive experiments on four benchmarks: VOT2016, UAV123, OTB-2015, and Temple-Color. When using expensive deep features, our tracker provides a 20-fold speedup and achieves a 13.0% relative gain in Expected Average Overlap compared to the top ranked method [12] in the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features, operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.

  • 6.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Accurate Scale Estimation for Robust Visual Tracking2014In: Proceedings of the British Machine Vision Conference 2014 / [ed] Michel Valstar, Andrew French and Tony Pridmore, BMVA Press , 2014Conference paper (Refereed)
    Abstract [en]

    Robust scale estimation is a challenging problem in visual object tracking. Most existing methods fail to handle large scale variations in complex image sequences. This paper presents a novel approach for robust scale estimation in a tracking-by-detection framework. The proposed approach works by learning discriminative correlation filters based on a scale pyramid representation. We learn separate filters for translation and scale estimation, and show that this improves the performance compared to an exhaustive scale search. Our scale estimation approach is generic as it can be incorporated into any tracking method with no inherent scale estimation.

    Experiments are performed on 28 benchmark sequences with significant scale variations. Our results show that the proposed approach significantly improves the performance by 18.8 % in median distance precision compared to our baseline. Finally, we provide both quantitative and qualitative comparison of our approach with state-of-the-art trackers in literature. The proposed method is shown to outperform the best existing tracker by 16.6 % in median distance precision, while operating at real-time.

  • 7.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking2016In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1430-1438Conference paper (Refereed)
    Abstract [en]

    Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

  • 8.
    Danelljan, Martin
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Häger, Gustav
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad Shahbaz
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Convolutional Features for Correlation Filter Based Visual Tracking2015In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), IEEE conference proceedings, 2015, p. 621-629Conference paper (Refereed)
    Abstract [en]

    Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they mitigate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard handcrafted features. Finally, results comparable to state-of-theart trackers are obtained on all three benchmark datasets.

  • 9.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Learning Spatially Regularized Correlation Filters for Visual Tracking2015In: Proceedings of the International Conference in Computer Vision (ICCV), 2015, IEEE Computer Society, 2015, p. 4310-4318Conference paper (Refereed)
    Abstract [en]

    Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model.

    We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.

  • 10.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Granström, Karl
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Heintz, Fredrik
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Rudol, Piotr
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Wzorek, Mariusz
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Kvarnström, Jonas
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology.
    A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

    In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

  • 11.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Meneghetti, Giulia
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    A Probabilistic Framework for Color-Based Point Set Registration2016In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1818-1826Conference paper (Refereed)
    Abstract [en]

    In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

  • 12.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Meneghetti, Giulia
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach2016In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 2016, IEEE, 2016, p. 247-252Conference paper (Refereed)
    Abstract [en]

    3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate high-dimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.

  • 13.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Robinson, Andreas
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking2016In: Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V / [ed] Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling, Cham: Springer, 2016, p. 472-488Conference paper (Refereed)
    Abstract [en]

    Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments.

  • 14.
    Danelljan, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Shahbaz Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    van de Weijer, Joost
    Computer Vision Center, CS Dept. Universitat Autonoma de Barcelona, Spain.
    Adaptive Color Attributes for Real-Time Visual Tracking2014In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014, IEEE Computer Society, 2014, p. 1090-1097Conference paper (Refereed)
    Abstract [en]

    Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.

    This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.

  • 15.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Berg, Amanda
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Systemteknik AB, Linköping, Sweden.
    Kristan, Matej
    University of Ljubljana, Slovenia.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Leonardis, Ales
    University of Birmingham, United Kingdom.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute of Technology, Austria.
    Vojır, Tomas
    Czech Technical University, Czech Republic.
    Nebehay, Georg
    Austrian Institute of Technology, Austria.
    Pflugfelder, Roman
    Austrian Institute of Technology, Austria.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Garcia-Martin8, Alvaro
    Universidad Autonoma de Madrid, Spain.
    Saffari, Amir
    Affectv, United Kingdom.
    Li, Ang
    Xi’an Jiaotong University.
    Solıs Montero, Andres
    University of Ottawa, Canada.
    Zhao, Baojun
    Beijing Institute of Technology, China.
    Schmid, Cordelia
    INRIA Grenoble Rhˆone-Alpes, France.
    Chen, Dapeng
    Xi’an Jiaotong University.
    Du, Dawei
    University at Albany, USA.
    Shahbaz Khan, Fahad
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    Australian National University, Australia.
    Zhu, Gao
    Australian National University, Australia.
    Zhu, Guibo
    NLPR, Chinese Academy of Sciences, China.
    Lu, Hanqing
    NLPR, Chinese Academy of Sciences, China.
    Kieritz, Hilke
    Fraunhofer IOSB, Germany.
    Li, Hongdong
    Australian National University, Australia.
    Qi, Honggang
    University at Albany, USA.
    Jeong, Jae-chan
    Electronics and Telecommunications Research Institute, Korea.
    Cho, Jae-il
    Electronics and Telecommunications Research Institute, Korea.
    Lee, Jae-Yeong
    Electronics and Telecommunications Research Institute, Korea.
    Zhu, Jianke
    Zhejiang University, China.
    Li, Jiatong
    University of Technology, Australia.
    Feng, Jiayi
    Institute of Automation, Chinese Academy of Sciences, China.
    Wang, Jinqiao
    NLPR, Chinese Academy of Sciences, China.
    Kim, Ji-Wan
    Electronics and Telecommunications Research Institute, Korea.
    Lang, Jochen
    University of Ottawa, Canada.
    Martinez, Jose M.
    Universidad Aut´onoma de Madrid, Spain.
    Xue, Kai
    INRIA Grenoble Rhˆone-Alpes, France.
    Alahari, Karteek
    INRIA Grenoble Rhˆone-Alpes, France.
    Ma, Liang
    Harbin Engineering University, China.
    Ke, Lipeng
    University at Albany, USA.
    Wen, Longyin
    University at Albany, USA.
    Bertinetto, Luca
    Oxford University, United Kingdom.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Tang, Ming
    Institute of Automation, Chinese Academy of Sciences, China.
    Chang, Ming-Ching
    University at Albany, USA.
    Miksik, Ondrej
    Oxford University, United Kingdom.
    Torr, Philip H S
    Oxford University, United Kingdom.
    Martin-Nieto, Rafael
    Universidad Aut´onoma de Madrid, Spain.
    Laganiere, Robert
    University of Ottawa, Canada.
    Hare, Sam
    Obvious Engineering, United Kingdom.
    Lyu, Siwei
    University at Albany, USA.
    Zhu, Song-Chun
    University of California, USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Hicks, Stephen L
    Oxford University, United Kingdom.
    Golodetz, Stuart
    Oxford University, United Kingdom.
    Choi, Sunglok
    Electronics and Telecommunications Research Institute, Korea.
    Wu, Tianfu
    University of California, USA.
    Hubner, Wolfgang
    Fraunhofer IOSB, Germany.
    Zhao, Xu
    Institute of Automation, Chinese Academy of Sciences, China.
    Hua, Yang
    INRIA Grenoble Rhˆone-Alpes, France.
    Li, Yang
    Zhejiang University, China.
    Lu, Yang
    University of California, USA.
    Li, Yuezun
    University at Albany, USA.
    Yuan, Zejian
    Xi’an Jiaotong University.
    Hong, Zhibin
    University of Technology, Australia.
    The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results2015In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 639-651Conference paper (Refereed)
    Abstract [en]

    The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

  • 16.
    Felsberg, Michael
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kristan, Matej
    University of Ljubljana, Slovenia.
    Matas, Jiri
    Czech Technical University, Czech Republic.
    Leonardis, Ales
    University of Birmingham, England.
    Pflugfelder, Roman
    Austrian Institute Technology, Austria.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Berg, Amanda
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision. Termisk Syst Tekn AB, Linkoping, Sweden.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Termisk Syst Tekn AB, Linkoping, Sweden.
    Cehovin, Luka
    University of Ljubljana, Slovenia.
    Vojir, Tomas
    Czech Technical University, Czech Republic.
    Lukezic, Alan
    University of Ljubljana, Slovenia.
    Fernandez, Gustavo
    Austrian Institute Technology, Austria.
    Petrosino, Alfredo
    Parthenope University of Naples, Italy.
    Garcia-Martin, Alvaro
    University of Autonoma Madrid, Spain.
    Solis Montero, Andres
    University of Ottawa, Canada.
    Varfolomieiev, Anton
    Kyiv Polytech Institute, Ukraine.
    Erdem, Aykut
    Hacettepe University, Turkey.
    Han, Bohyung
    POSTECH, South Korea.
    Chang, Chang-Ming
    University of Albany, GA USA.
    Du, Dawei
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Erdem, Erkut
    Hacettepe University, Turkey.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Porikli, Fatih
    ARC Centre Excellence Robot Vis, Australia; CSIRO, Australia.
    Zhao, Fei
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Bunyak, Filiz
    University of Missouri, MO 65211 USA.
    Battistone, Francesco
    Parthenope University of Naples, Italy.
    Zhu, Gao
    University of Missouri, Columbia, USA.
    Seetharaman, Guna
    US Navy, DC 20375 USA.
    Li, Hongdong
    ARC Centre Excellence Robot Vis, Australia.
    Qi, Honggang
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Bischof, Horst
    Graz University of Technology, Austria.
    Possegger, Horst
    Graz University of Technology, Austria.
    Nam, Hyeonseob
    NAVER Corp, South Korea.
    Valmadre, Jack
    University of Oxford, England.
    Zhu, Jianke
    Zhejiang University, Peoples R China.
    Feng, Jiayi
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Lang, Jochen
    University of Ottawa, Canada.
    Martinez, Jose M.
    University of Autonoma Madrid, Spain.
    Palaniappan, Kannappan
    University of Missouri, MO 65211 USA.
    Lebeda, Karel
    University of Surrey, England.
    Gao, Ke
    University of Missouri, MO 65211 USA.
    Mikolajczyk, Krystian
    Imperial Coll London, England.
    Wen, Longyin
    University of Albany, GA USA.
    Bertinetto, Luca
    University of Oxford, England.
    Poostchi, Mahdieh
    University of Missouri, MO 65211 USA.
    Maresca, Mario
    Parthenope University of Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Arens, Michael
    Fraunhofer IOSB, Germany.
    Tang, Ming
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Baek, Mooyeol
    POSTECH, South Korea.
    Fan, Nana
    Harbin Institute Technology, Peoples R China.
    Al-Shakarji, Noor
    University of Missouri, MO 65211 USA.
    Miksik, Ondrej
    University of Oxford, England.
    Akin, Osman
    Hacettepe University, Turkey.
    Torr, Philip H. S.
    University of Oxford, England.
    Huang, Qingming
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Martin-Nieto, Rafael
    University of Autonoma Madrid, Spain.
    Pelapur, Rengarajan
    University of Missouri, MO 65211 USA.
    Bowden, Richard
    University of Surrey, England.
    Laganiere, Robert
    University of Ottawa, Canada.
    Krah, Sebastian B.
    Fraunhofer IOSB, Germany.
    Li, Shengkun
    University of Albany, GA USA.
    Yao, Shizeng
    University of Missouri, MO 65211 USA.
    Hadfield, Simon
    University of Surrey, England.
    Lyu, Siwei
    University of Albany, GA USA.
    Becker, Stefan
    Fraunhofer IOSB, Germany.
    Golodetz, Stuart
    University of Oxford, England.
    Hu, Tao
    Australian National University, Australia; Chinese Academic Science, Peoples R China.
    Mauthner, Thomas
    Graz University of Technology, Austria.
    Santopietro, Vincenzo
    Parthenope University of Naples, Italy.
    Li, Wenbo
    Lehigh University, PA 18015 USA.
    Huebner, Wolfgang
    Fraunhofer IOSB, Germany.
    Li, Xin
    Harbin Institute Technology, Peoples R China.
    Li, Yang
    Zhejiang University, Peoples R China.
    Xu, Zhan
    Zhejiang University, Peoples R China.
    He, Zhenyu
    Harbin Institute Technology, Peoples R China.
    The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results2016In: Computer Vision – ECCV 2016 Workshops. ECCV 2016. / [ed] Hua G., Jégou H., SPRINGER INT PUBLISHING AG , 2016, p. 824-849Conference paper (Refereed)
    Abstract [en]

    The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

  • 17.
    Gladh, Susanna
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad Shahbaz
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Deep motion features for visual tracking2016In: Proceedings of the 23rd International Conference on, Pattern Recognition (ICPR), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1243-1248Conference paper (Refereed)
    Abstract [en]

    Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.

  • 18.
    Häger, Gustav
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Bhat, Goutam
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Khan, Fahad Shahbaz
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
    Rudol, Piotr
    Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, The Institute of Technology.
    Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV2016In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper (Refereed)
    Abstract [en]

    Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.

    Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.

  • 19.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    On the Optimization of Advanced DCF-Trackers2018In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part I / [ed] Laura Leal-TaixéStefan Roth, Cham: Springer Publishing Company, 2018, p. 54-69Conference paper (Refereed)
    Abstract [en]

    Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core. DCF-based trackers interleave learning of the target detector and target state inference based on this detector. Whereas the original formulation includes a closed-form solution for the filter learning, recently introduced improvements to the framework no longer have known closed-form solutions. Instead a large-scale linear least squares problem must be solved each time the detector is updated. We analyze the procedure used to optimize the detector and let the popular scheme introduced with ECO serve as a baseline. The ECO implementation is revisited in detail and several mechanisms are provided with alternatives. With comprehensive experiments we show which configurations are superior in terms of tracking capabilities and optimization performance.

  • 20.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Zenuity, Sweden.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. ETH Zurich, Switzerland.
    Brissman, Emil
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. Saab, Sweden.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. IIAI, UAE.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    A generative appearance model for end-to-end video object segmentation2019In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 8945-8954Conference paper (Refereed)
    Abstract [en]

    One of the fundamental challenges in video object segmentation is to find an effective representation of the target and background appearance. The best performing approaches resort to extensive fine-tuning of a convolutional neural network for this purpose. Besides being prohibitively expensive, this strategy cannot be truly trained end-to-end since the online fine-tuning procedure is not integrated into the offline training of the network. To address these issues, we propose a network architecture that learns a powerful representation of the target and background appearance in a single forward pass. The introduced appearance module learns a probabilistic generative model of target and background feature distributions. Given a new image, it predicts the posterior class probabilities, providing a highly discriminative cue, which is processed in later network modules. Both the learning and prediction stages of our appearance module are fully differentiable, enabling true end-to-end training of the entire segmentation pipeline. Comprehensive experiments demonstrate the effectiveness of the proposed approach on three video object segmentation benchmarks. We close the gap to approaches based on online fine-tuning on DAVIS17, while operating at 15 FPS on a single GPU. Furthermore, our method outperforms all published approaches on the large-scale YouTube-VOS dataset.

  • 21.
    Johnander, Joakim
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    DCCO: Towards Deformable Continuous Convolution Operators for Visual Tracking2017In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, Vol. 10424, p. 55-67Conference paper (Refereed)
    Abstract [en]

    Discriminative Correlation Filter (DCF) based methods have shown competitive performance on tracking benchmarks in recent years. Generally, DCF based trackers learn a rigid appearance model of the target. However, this reliance on a single rigid appearance model is insufficient in situations where the target undergoes non-rigid transformations. In this paper, we propose a unified formulation for learning a deformable convolution filter. In our framework, the deformable filter is represented as a linear combination of sub-filters. Both the sub-filter coefficients and their relative locations are inferred jointly in our formulation. Experiments are performed on three challenging tracking benchmarks: OTB-2015, TempleColor and VOT2016. Our approach improves the baseline method, leading to performance comparable to state-of-the-art.

  • 22.
    Järemo Lawin, Felix
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Forssén, Per-Erik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Density Adaptive Point Set Registration2018In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, p. 3829-3837Conference paper (Refereed)
    Abstract [en]

    Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets.    We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real-world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

  • 23.
    Järemo-Lawin, Felix
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Tosteberg, Patrik
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Deep Projective 3D Semantic Segmentation2017In: Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I / [ed] Michael Felsberg, Anders Heyden and Norbert Krüger, Springer, 2017, p. 95-107Conference paper (Refereed)
    Abstract [en]

    Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets.

  • 24.
    Kristan, Matej
    et al.
    University of Ljubljana, Slovenia.
    Leonardis, Aleš
    University of Birmingham, United Kingdom.
    Matas, Jirí
    Czech Technical University, Czech Republic.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Pflugfelder, Roman
    Austrian Institute of Technology, Austria / TU Wien, Austria.
    Zajc, Luka Cehovin
    University of Ljubljana, Slovenia.
    Vojírì, Tomáš
    Czech Technical University, Czech Republic.
    Bhat, Goutam
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Lukezič, Alan
    University of Ljubljana, Slovenia.
    Eldesokey, Abdelrahman
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Fernández, Gustavo
    García-Martín, Álvaro
    Iglesias-Arias, Álvaro
    Alatan, A. Aydin
    González-García, Abel
    Petrosino, Alfredo
    Memarmoghadam, Alireza
    Vedaldi, Andrea
    Muhič, Andrej
    He, Anfeng
    Smeulders, Arnold
    Perera, Asanka G.
    Li, Bo
    Chen, Boyu
    Kim, Changick
    Xu, Changsheng
    Xiong, Changzhen
    Tian, Cheng
    Luo, Chong
    Sun, Chong
    Hao, Cong
    Kim, Daijin
    Mishra, Deepak
    Chen, Deming
    Wang, Dong
    Wee, Dongyoon
    Gavves, Efstratios
    Gundogdu, Erhan
    Velasco-Salido, Erik
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Fan
    Zhao, Fei
    Li, Feng
    Battistone, Francesco
    De Ath, George
    Subrahmanyam, Gorthi R. K. S.
    Bastos, Guilherme
    Ling, Haibin
    Galoogahi, Hamed Kiani
    Lee, Hankyeol
    Li, Haojie
    Zhao, Haojie
    Fan, Heng
    Zhang, Honggang
    Possegger, Horst
    Li, Houqiang
    Lu, Huchuan
    Zhi, Hui
    Li, Huiyun
    Lee, Hyemin
    Chang, Hyung Jin
    Drummond, Isabela
    Valmadre, Jack
    Martin, Jaime Spencer
    Chahl, Javaan
    Choi, Jin Young
    Li, Jing
    Wang, Jinqiao
    Qi, Jinqing
    Sung, Jinyoung
    Johnander, Joakim
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Henriques, Joao
    Choi, Jongwon
    van de Weijer, Joost
    Herranz, Jorge Rodríguez
    Martínez, José M.
    Kittler, Josef
    Zhuang, Junfei
    Gao, Junyu
    Grm, Klemen
    Zhang, Lichao
    Wang, Lijun
    Yang, Lingxiao
    Rout, Litu
    Si, Liu
    Bertinetto, Luca
    Chu, Lutao
    Che, Manqiang
    Maresca, Mario Edoardo
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yang, Ming-Hsuan
    Abdelpakey, Mohamed
    Shehata, Mohamed
    Kang, Myunggu
    Lee, Namhoon
    Wang, Ning
    Miksik, Ondrej
    Moallem, P.
    Vicente-Moñivar, Pablo
    Senna, Pedro
    Li, Peixia
    Torr, Philip
    Raju, Priya Mariam
    Ruihe, Qian
    Wang, Qiang
    Zhou, Qin
    Guo, Qing
    Martín-Nieto, Rafael
    Gorthi, Rama Krishna
    Tao, Ran
    Bowden, Richard
    Everson, Richard
    Wang, Runling
    Yun, Sangdoo
    Choi, Seokeon
    Vivas, Sergio
    Bai, Shuai
    Huang, Shuangping
    Wu, Sihang
    Hadfield, Simon
    Wang, Siwen
    Golodetz, Stuart
    Ming, Tang
    Xu, Tianyang
    Zhang, Tianzhu
    Fischer, Tobias
    Santopietro, Vincenzo
    Štruc, Vitomir
    Wei, Wang
    Zuo, Wangmeng
    Feng, Wei
    Wu, Wei
    Zou, Wei
    Hu, Weiming
    Zhou, Wengang
    Zeng, Wenjun
    Zhang, Xiaofan
    Wu, Xiaohe
    Wu, Xiao-Jun
    Tian, Xinmei
    Li, Yan
    Lu, Yan
    Law, Yee Wei
    Wu, Yi
    Demiris, Yiannis
    Yang, Yicai
    Jiao, Yifan
    Li, Yuhong
    Zhang, Yunhua
    Sun, Yuxuan
    Zhang, Zheng
    Zhu, Zheng
    Feng, Zhen-Hua
    Wang, Zhihui
    He, Zhiqun
    The Sixth Visual Object Tracking VOT2018 Challenge Results2018In: Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8–14, 2018 Proceedings, Part I / [ed] Laura Leal-Taixé and Stefan Roth, Cham: Springer Publishing Company, 2018, p. 3-53Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

  • 25.
    Kristan, Matej
    et al.
    University of Ljubljana, Ljubljana, Slovenia.
    Pflugfelder, Roman P.
    Austrian Institute of Technology, Vienna, Austria.
    Leonardis, Ales
    University of Birmingham, Birmingham, UK.
    Matas, Jiri
    Czech Technical University, Prague, Czech Republic.
    Cehovin, Luka
    University of Ljubljana, Ljubljana, Slovenia.
    Nebehay, Georg
    Austrian Institute of Technology, Vienna, Austria.
    Vojir, Tomas
    Czech Technical University, Prague, Czech Republic.
    Fernandez, Gustavo
    Austrian Institute of Technology, Vienna, Austria.
    Lukezi, Alan
    University of Ljubljana, Ljubljana, Slovenia.
    Dimitriev, Aleksandar
    University of Ljubljana, Ljubljana, Slovenia.
    Petrosino, Alfredo
    Parthenope University of Naples, Naples, Italy.
    Saffari, Amir
    Affectv Limited, London, UK.
    Li, Bo
    Panasonic R&D Center, Singapore, Singapore.
    Han, Bohyung
    POSTECH, Pohang, Korea.
    Heng, CherKeng
    Panasonic R&D Center, Singapore, Singapore.
    Garcia, Christophe
    LIRIS, Lyon, France.
    Pangersic, Dominik
    University of Ljubljana, Ljubljana, Slovenia.
    Häger, Gustav
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Khan, Fahad Shahbaz
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Oven, Franci
    University of Ljubljana, Ljubljana, Slovenia.
    Possegger, Horst
    Graz University of Technology, Graz, Austria.
    Bischof, Horst
    Graz University of Technology, Graz, Austria.
    Nam, Hyeonseob
    POSTECH, Pohang, Korea.
    Zhu, Jianke
    Zhejiang University, Hangzhou, China.
    Li, JiJia
    Shanghai Jiao Tong University, Shanghai, China.
    Choi, Jin Young
    ASRI Seoul National University, Gwanak, Korea.
    Choi, Jin-Woo
    Electronics and Telecommunications Research Institute, Daejeon, Korea.
    Henriques, Joao F.
    University of Coimbra, Coimbra, Portugal.
    van de Weijer, Joost
    Universitat Autonoma de Barcelona, Barcelona, Spain.
    Batista, Jorge
    University of Coimbra, Coimbra, Portugal.
    Lebeda, Karel
    University of Surrey, Surrey, UK.
    Ofjall, Kristoffer
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Yi, Kwang Moo
    EPFL CVLab, Lausanne, Switzerland.
    Qin, Lei
    ICT CAS, Beijing, China.
    Wen, Longyin
    Chinese Academy of Sciences, Beijing, China.
    Maresca, Mario Edoardo
    Parthenope University of Naples, Naples, Italy.
    Danelljan, Martin
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Felsberg, Michael
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
    Cheng, Ming-Ming
    University of Oxford, Oxford, UK.
    Torr, Philip
    University of Oxford, Oxford, UK.
    Huang, Qingming
    Harbin Institute of Technology, Harbin, China.
    Bowden, Richard
    University of Surrey, Surrey, UK.
    Hare, Sam
    Obvious Engineering Limited, London, UK.
    YueYing Lim, Samantha
    Panasonic R&D Center, Singapore, Singapore.
    Hong, Seunghoon
    POSTECH, Pohang, Korea.
    Liao, Shengcai
    Chinese Academy of Sciences, Beijing, China.
    Hadfield, Simon
    University of Surrey, Surrey, UK.
    Li, Stan Z.
    Chinese Academy of Sciences, Beijing, China.
    Duffner, Stefan
    LIRIS, Lyon, France.
    Golodetz, Stuart
    University of Oxford, Oxford, UK.
    Mauthner, Thomas
    Graz University of Technology, Graz, Austria.
    Vineet, Vibhav
    University of Oxford, Oxford, UK.
    Lin, Weiyao
    Shanghai Jiao Tong University, Shanghai, China.
    Li, Yang
    Zhejiang University, Hangzhou, China.
    Qi, Yuankai
    Harbin Institute of Technology, Harbin, China.
    Lei, Zhen
    Chinese Academy of Sciences, Beijing, China.
    Niu, ZhiHeng
    Panasonic R&D Center, Singapore, Singapore.
    The Visual Object Tracking VOT2014 Challenge Results2015In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT II, Springer, 2015, Vol. 8926, p. 191-217Conference paper (Refereed)
    Abstract [en]

    The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://​votchallenge.​net).

  • 26.
    Meneghetti, Giulia
    et al.
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Danelljan, Martin
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Felsberg, Michael
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Nordberg, Klas
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Computer Vision.
    Image alignment for panorama stitching in sparsely structured environments2015In: Image Analysis. SCIA 2015. / [ed] Paulsen, Rasmus R., Pedersen, Kim S., Springer, 2015, p. 428-439Conference paper (Refereed)
    Abstract [en]

    Panorama stitching of sparsely structured scenes is an open research problem. In this setting, feature-based image alignment methods often fail due to shortage of distinct image features. Instead, direct image alignment methods, such as those based on phase correlation, can be applied. In this paper we investigate correlation-based image alignment techniques for panorama stitching of sparsely structured scenes. We propose a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking. Two versions of the proposed DCF-based approach are evaluated on two real and one synthetic panorama dataset of sparsely structured indoor environments. All three datasets consist of images taken on a tripod rotating 360 degrees around the vertical axis through the optical center. We show that the proposed DCF-based methods outperform phase correlation-based approaches on these datasets.

1 - 26 of 26
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