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Multi-pedestrian Tracking Based on Social Forces
Ren HL(任恒乐)1,2; Xu F(徐方)3; Zou FS(邹风山)3; Jia K(贾凯)3; Di, Pei3; Kang J(康杰)1,2
Conference Name2018 International Conference on Intelligence and Safety for Robotics, ISR 2018
Conference DateAugust 24-27, 2018
Conference PlaceShenyang, China
Source Publication2018 International Conference on Intelligence and Safety for Robotics, ISR 2018
Publication PlaceNew York
Indexed ByEI ; CPCI(ISTP)
EI Accession number20185206309947
WOS IDWOS:000455843900092
Contribution Rank1
AbstractMulti-pedestrian tracking based on video has always faced many problems. Tracking-by-detection paradigm is a popular method to solve these problems. For example, due to the influence of sensors, lighting, background, detection may result in some false detections and missed detections. In order to solve this problem, in this paper, we propose a new tracking method based on the social force model. Here, pedestrians are divided into two categories: candidate pedestrians and real pedestrians. The real pedestrians are the pedestrians we want to track. Both can be transformed into each other by their respective historical records. The social force model is used to predict the position of each person in the next frame, and the weighted distance between the detected pedestrian in the current frame and the detection in the next frame of image is calculated. According to the distance matrix, the Hungarian algorithm is used to assign identities so as to achieve the purpose of multi-pedestrian tracking. Our results were evaluated on the MOT challenges dataset and compared with existing advanced algorithms. The results show that this method outperforms traditional algorithms in the number of mostly tracked (MT), mostly lost (ML) and the number of frames processed per second (FPS). Including Particle filter, traditional social force model and Kalman filter algorithm tracking method. © 2018 IEEE.
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Document Type会议论文
Corresponding AuthorRen HL(任恒乐)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Shenyang SIASUN Robot Automation Co. LTD. China, Shenyang 110168, China
Recommended Citation
GB/T 7714
Ren HL,Xu F,Zou FS,et al. Multi-pedestrian Tracking Based on Social Forces[C]. New York:IEEE,2018:527-532.
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