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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 6
1626-1632

Design of TOC reward function in multi-target trajectory recovery with deep reinforcement learning

He Liang1
Xu Zhengguo1
Jia Yu1
Shen Chao2
Li Yun1
1. National Key Laboratory of Science & Technology on Blind Signal Processing, Chengdu 610041, China
2. MOE Key Laboratory for Intelligent Networks & Network Security, Xi'an Jiaotong University, Xi'an 710049, China
DOI: 10.19734/j.issn.1001-3695.2018.12.0886

Abstract

It attracts lots of attention in the field of object trajectory detection that detectors always receive several geographical locations without any other information about the targets, and furthermore it comes into a problem to use the geographical location information received by the sensors to reconstruct the trajectories of each target as well as to distinguish the targets in each frame, which is called multi-target trajectory recovery and can be solved by deep reinforcement learning(DRL). This paper implemented a trajectory osculating circle(TOC) reward function based on the mathematical model of the direction and trajectory curvature according to the peculiarity of trajectories in actual. Firstly, it switched the issue of the multi-target trajectory reconstruction into a model which could be appropriate for DRL. Then, it tested DRL with the proposed reward function. Finally, it introduced a mathematical derivation and physical interpretation of the proposed TOC reward function. The experimental result shows that DRL with the TOC reward function can reverse the trajectory effectively, and the trace corresponds well with the actual trajectory.

Foundation Support

国家自然科学基金重点项目(U1736205)
国家自然科学基金资助项目(61773310)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.12.0886
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 6
Section: Algorithm Research & Explore
Pages: 1626-1632
Serial Number: 1001-3695(2020)06-004-1626-07

Publish History

[2020-06-05] Printed Article

Cite This Article

贺亮, 徐正国, 贾愚, 等. 深度强化学习复原多目标航迹的TOC奖励函数 [J]. 计算机应用研究, 2020, 37 (6): 1626-1632. (He Liang, Xu Zhengguo, Jia Yu, et al. Design of TOC reward function in multi-target trajectory recovery with deep reinforcement learning [J]. Application Research of Computers, 2020, 37 (6): 1626-1632. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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