Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint

YANG Honghong; SHANG Junchao; LI Jingjing; ZHANG Yumei; WU Xiaojun

doi:10.1049/cje.2020.00.007

Volume 31 Issue 2

Mar. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(2): 266-276

YANG Honghong, SHANG Junchao, LI Jingjing, et al. “Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint”. Chinese Journal of Electronics, vol. 31 no. 2. doi: 10.1049/cje.2020.00.007

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YANG Honghong, SHANG Junchao, LI Jingjing, et al. “Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint”. Chinese Journal of Electronics, vol. 31 no. 2. doi: 10.1049/cje.2020.00.007

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Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint

doi: 10.1049/cje.2020.00.007

YANG Honghong^{1, 2
,},
SHANG Junchao^1
,,
LI Jingjing^1
,,
ZHANG Yumei^{1, 2
,},
WU Xiaojun^{1, 2, 3
,}

1.
Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an 710062, China
2.
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
3.
School of Journalism and Communication, Shaanxi Normal University, Xi’an 710062, China

Funds: This work was supported by the National Natural Science Foundation of China (61907028, 11872036, 11772178, 61971273), the Young Talent Fund of University Association for Science and Technology in Shaanxi (20200105), the China Postdoctoral Science Foundation (2018M640950), and the Natural Science Foundation of Shaanxi Provincial (2019JQ-574, 2019GY-217).

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Author Bio:
received the M.S. and Ph.D. degrees from the Department of Automation, Northwestern Polytechnical University, Xi’an, China, in 2014 and 2018, respectively. She is an Associate Professor at the Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an. Her research interests include computer vision and object tracking and detection. (Email: yanghonghong0615@163.com)

received the B.S. degree, in 2018. He is currently pursuing the M.S. degree with Shaanxi Normal University. His research interests include artificial intelligence and object tracking and detection. (Email: junchaoshang@foxmail.com)

received the B.S. degree in 2013. She is currently a Ph.D. candidate in Shaanxi Normal University, Xi’an, China. Her research interests include artificial intelligence and signal analysis. (Email: jingjl101@snnu.edu.cn)

received the Ph.D. degree in control engineering from Northwestern Polytechnical University, Xi’an, China, in 2009. Currently, she is a Professor at Shaanxi Normal University. Her research interests include signal processing and chaotic signal analysis. (Email: zym0910@snnu.edu.cn)

(corresponding author) received the Ph.D. degree in system engineering from Northwestern Polytechnical University, Xi’an, China, in 2005. He is a Professor at Shaanxi Normal University. His research interests include pattern recognition, intelligent system and system complexity. (Email: xjwu@snnu.edu.cn)
Received Date: 2019-12-20
Accepted Date: 2020-06-02

Available Online: 2021-09-28

Publish Date: 2022-03-05

Abstract

Abstract

Vehicles or pedestrians tracking is an important task in intelligent transportation system. In this paper, we propose an online multi-object tracking for intelligent traffic platform that employs improved sparse representation and structural constraint. We first build the spatial-temporal constraint via the geometric relations and appearance of tracked objects, then we construct a robust appearance model by incorporating the discriminative sparse representation with weight constraint and local sparse appearance with occlusion analysis. Finally, we complete data association by using maximum a posteriori in a Bayesian framework in the pursuit for the optimal detection estimation. Experimental results in two challenging vehicle tracking benchmark datasets show that the proposed method has a good tracking performance.
- Intelligent vehicles,
- Multi-traffic object tracking,
- Sparse representation,
- Spatial-temporal constraint

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References(32)

References

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