Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning

Ming-xin Jiang, Chao Deng, Zhi-geng Pan, Lan-fang Wang and Xing Sun

Complexity, 2018, vol. 2018, 1-12

Abstract:

Multiple-object tracking is a challenging issue in the computer vision community. In this paper, we propose a multiobject tracking algorithm in videos based on long short-term memory (LSTM) and deep reinforcement learning. Firstly, the multiple objects are detected by the object detector YOLO V2. Secondly, the problem of single-object tracking is considered as a Markov decision process (MDP) since this setting provides a formal strategy to model an agent that makes sequence decisions. The single-object tracker is composed of a network that includes a CNN followed by an LSTM unit. Each tracker, regarded as an agent, is trained by utilizing deep reinforcement learning. Finally, we conduct a data association using LSTM for each frame between the results of the object detector and the results of single-object trackers. From the experimental results, we can see that our tracker achieves better performance than the other state-of-the-art methods. Multiple targets can be steadily tracked even when frequent occlusions, similar appearances, and scale changes happened.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4695890

DOI: 10.1155/2018/4695890

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