Abstract
Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.
Highlights
There are various Multiple Object Tracking (MOT)-related benchmarks, this paper focuses on the MOT
Multiple Object Tracking Precision (MOTP) can be computed to check whether the tracker performed properly or not
This research is helpful for readers who are interested in studying the object tracking problem, especially MOT
Summary
Academic Editors: Amir Mosavi and Jungong Han. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Has revolutionized several fields, such as computer vision and natural language processing (NLP). Object detection [6,7] is a well-developed field in computer vision. Object tracking is usually the process after object detection, which receives an initial set of detected objects, puts a unique identification (ID) for each of the initial detections, and tracks the detected objects as they move between frames. Multiple Object Tracking (MOT) is a subgroup of object tracking, which is proposed to track multiple objects in a video and represent them as a set of trajectories with high accuracy
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