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Article

SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking

1
Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
2
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Xian Tao, Qingyi Gu and Hu Su
Sensors 2022, 22(15), 5863; https://doi.org/10.3390/s22155863 (registering DOI)
Received: 4 July 2022 / Revised: 26 July 2022 / Accepted: 2 August 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Intelligent Vision Technology/ Sensors for Industrial Applications)
Joint detection and embedding (JDE) methods usually fuse the target motion information and appearance information as the data association matrix, which could fail when the target is briefly lost or blocked in multi-object tracking (MOT). In this paper, we aim to solve this problem by proposing a novel association matrix, the Embedding and GioU (EG) matrix, which combines the embedding cosine distance and GioU distance of objects. To improve the performance of data association, we develop a simple, effective, bottom-up fusion tracker for re-identity features, named SimpleTrack, and propose a new tracking strategy which can mitigate the loss of detection targets. To show the effectiveness of the proposed method, experiments are carried out using five different state-of-the-art JDE-based methods. The results show that by simply replacing the original association matrix with our EG matrix, we can achieve significant improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of these methods by around 20%. In addition, our SimpleTrack has the best data association capability among the JDE-based methods, e.g., 61.6 HOTA and 76.3 IDF1, on the test set of MOT17 with 23 FPS running speed on a single GTX2080Ti GPU. View Full-Text
Keywords: multiple object tracking; association matrix; joint detection and embedding; decoupling representation multiple object tracking; association matrix; joint detection and embedding; decoupling representation
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MDPI and ACS Style

Li, J.; Ding, Y.; Wei, H.-L.; Zhang, Y.; Lin, W. SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking. Sensors 2022, 22, 5863. https://doi.org/10.3390/s22155863

AMA Style

Li J, Ding Y, Wei H-L, Zhang Y, Lin W. SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking. Sensors. 2022; 22(15):5863. https://doi.org/10.3390/s22155863

Chicago/Turabian Style

Li, Jiaxin, Yan Ding, Hua-Liang Wei, Yutong Zhang, and Wenxiang Lin. 2022. "SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking" Sensors 22, no. 15: 5863. https://doi.org/10.3390/s22155863

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