In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
296-302

Multi-target motion trajectory prediction algorithm based on deep learning

Ren Tiaojuan1,2
Chen Peng2
Chen Yourong1,2
Liu Banteng1
Sun Ping1
1. School of Information Science & Technology, Zhejiang Shuren University, Hangzhou 310015, China
2. School of Computer & Artificial Intelligence, Changzhou University, Changzhou Jiangshu 213164, China
DOI: 10.19734/j.issn.1001-3695.2021.05.0203

Abstract

In the process of multi-target motion trajectory prediction, due to insufficient detection accuracy and real-time performance, some target position information is lost and prediction accuracy is not high. This paper proposed a multi-target trajectory prediction(MMTP) algorithm based on improved Kalman filter. MMTP algorithm used the YOLOv4 detector in the target detection stage to improve the accuracy and speed of target detection. In the target matching stage, MMTP algorithm used the KM matching algorithm to associate the detection target of the current detection frame with the target of the prediction frame predicted at the previous moment, thereby enhancing the accuracy of target association and avoiding target loss caused by target occlusion, target interleaving and drift. In the target coordinate prediction stage, this paper proposed an improved Kalman filter algorithm to predict the position coordinates of the next frame for each moving target and draw a prediction frame, so as to improve the prediction accuracy of target coordinates in non-linear scenes and reduce the error of predicted coordinates. Then it used the video sequence data set taken by MOT16 and the actual traffic system to verify the overall performance of the algorithm. The simulation results show that MMTP algorithm has better detection accuracy and speed in the target detection stage, which effectively improves the overall operating speed of the algorithm. In the target matching stage, MMTP algorithm can enhance the accuracy of target association and reduce target loss, which is better than RMOT, POI, SORT, Deep-SORT and YVTP.

Foundation Support

浙江树人大学省属高校基本科研经费与专项资金资助项目(2021XZ018)
浙江省公益技术应用研究项目(LGF21F01004)
浙江省教育厅科研项目(Y201942981)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.05.0203
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Technology of Graphic & Image
Pages: 296-302
Serial Number: 1001-3695(2022)01-053-0296-07

Publish History

[2022-01-05] Printed Article

Cite This Article

任条娟, 陈鹏, 陈友荣, 等. 基于深度学习的多目标运动轨迹预测算法 [J]. 计算机应用研究, 2022, 39 (1): 296-302. (Ren Tiaojuan, Chen Peng, Chen Yourong, et al. Multi-target motion trajectory prediction algorithm based on deep learning [J]. Application Research of Computers, 2022, 39 (1): 296-302. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)