Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker

• Published in: IEEE access Vol. 7; pp. 32400 - 32407
• Main Authors: Jiang, Mingxin, Hai, Tao, Pan, Zhigeng, Wang, Haiyan, Jia, Yinjie, Deng, Chao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computer vision; Deep learning; Distance learning; IQL; MADRL; Multi-object tracking; Multiagent systems; Multiple target tracking; Neural networks; Object detection; Object recognition; Real-time systems; Reinforcement learning; Target tracking; Training; YOLO V3
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2901300

Multi-object tracking has been a key research subject in many computer vision applications. We propose a novel approach based on multi-agent deep reinforcement learning (MADRL) for multi-object tracking to solve the problems in the existing tracking methods, such as a varying number of targets, non-causal, and non-realtime. At first, we choose YOLO V3 to detect the objects included in each frame. Unsuitable candidates were screened out and the rest of detection results are regarded as multiple agents and forming a multi-agent system. Independent Q-Learners (IQL) is used to learn the agents' policy, in which, each agent treats other agents as part of the environment. Then, we conducted offline learning in the training and online learning during the tracking. Our experiments demonstrate that the use of MADRL achieves better performance than the other state-of-art methods in precision, accuracy, and robustness.