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Unveiling the power of deep tracking
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.
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2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Cham: Springer Publishing Company, 2018. p. 493-509
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11206
National Category
Computer Vision and Robotics (Autonomous Systems) Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-161032DOI: 10.1007/978-3-030-01216-8_30ISBN: 9783030012151 (print)ISBN: 9783030012168 (electronic)OAI: oai:DiVA.org:liu-161032DiVA, id: diva2:1361991
Conference
15th European Conference on Computer Vision (ECCV). Munich, Germany, 8-14 September, 2018
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-30Bibliographically approved

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