Journal
NEUROCOMPUTING
Volume 381, Issue -, Pages 61-88Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2019.11.023
Keywords
Multiple object tracking; Deep learning; Video tracking; Convolutional neural networks; LSTM; Reinforcement learning
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Funding
- Spanish Ministry of Science and Technology [TIN2017-89517P]
- Ramon y Cajal Programme [RYC-2015-18136]
- project DeepSCOP-Ayudas Fundacion BBVA a Equipos de Investigacion Cientifica en Big Data 2018
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The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions. (C) 2019 Elsevier B.V. All rights reserved.
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