Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.

Object detection is a critical task in computer vision with applications across various domains, ranging from autonomous driving to surveillance systems. Despite extensive research on improving the performance of object detection systems, identifying all objects in different places remains a challenge. The traditional object detection approaches focus primarily on extracting and analyzing visual features without considering the contextual information about the places of objects. However, entities in many real-world scenarios closely relate to their surrounding environment, providing crucial contextual cues for accurate detection. This study investigates the importance and impact of places of images (indoor and outdoor) on object detection accuracy. To this purpose, we propose an approach that first categorizes images into two distinct categories: indoor and outdoor. We then train and evaluate three object detection models (indoor, outdoor, and general models) based on YOLOv5 and 19 classes of the PASCAL VOC dataset and 79 classes of COCO dataset that consider places. The experimental evaluations show that the specialized indoor and outdoor models have higher mAP (mean Average Precision) to detect objects in specific environments compared to the general model that detects objects found both indoors and outdoors. Indeed, the network can detect objects more accurately in similar places with common characteristics due to semantic relationships between objects and their surroundings, and the network’s misdetection is diminished. All the results were analyzed statistically with t-tests.

This paper presents a novel Hierarchical Transfer and Multi-task Learning (HTMTL) approach designed to substantially improve the performance of scene classification networks by leveraging the collective influence of diverse scene types. HTMTL is distinguished by its ability to capture the interaction between various scene types, recognizing how context information from one scene category can enhance the classification performance of another. Our method, when applied to the Places365 dataset, demonstrates a significant improvement in the network’s ability to accurately identify scene types. By exploiting these inter-scene interactions, HTMTL significantly enhances scene classification performance, making it a potent tool for advancing scene understanding and classification. Additionally, this study explores the contribution of individual tasks and task groupings on the performance of other tasks. To further validate the generality of HTMTL, we applied it to the Cityscapes dataset, where the results also show promise. This indicates the broad applicability and effectiveness of our approach across different datasets and scene types.

Falls frequently present substantial safety hazards to those who are alone, particularly the elderly. Deploying a rapid and proficient method for detecting falls is a highly effective approach to tackle this concealed peril. The majority of existing fall detection methods rely on either visual data or wearable devices, both of which have drawbacks. This research presents a multimodal approach that integrates video and audio modalities to address the issue of fall detection systems and enhances the accuracy of fall detection in challenging environmental conditions. This multimodal approach, which leverages the benefits of attention mechanism in both video and audio streams, utilizes features from both modalities through feature-level fusion to detect falls in unfavorable conditions where visual systems alone are unable to do so. We assessed the performance of our multimodal fall detection model using Le2i and UP-Fall datasets. Additionally, we compared our findings with other fall detection methods. The outstanding results of our multimodal model indicate its superior performance compared to single fall detection models.