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Real-Time Multi-person Multi-camera Tracking Based on Improved Matching Cascade

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Advances in Intelligent Systems and Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 268))

Abstract

In a small-scale distributed multi-camera system like video surveillance system of a museum, shopping mall, plaza, etc., or advanced driving assistance system, real-time multi-person tracking is essential for public and pedestrian safety consideration in the smart security system. In this paper, a real-time multi-person multi-camera tracking framework is presented, which is compatible with both overlapping and non-overlapping views. Since cameras have different orientations and exposures, false matching occurs frequently when people cross the camera boundaries or reenter the same camera. To deal with this challenge, an improved multi-person multi-camera matching cascade scheme is proposed, which can increase the accuracy of inter-camera person re-identification (Re-ID) by taking advantage of association priorities of targets and features. Besides, the proposed method can deal with the occlusion of people and variation of appearance features. Experiments are implemented with overlapping and non-overlapping videos, and results show that the proposed method has robust performance in different situations.

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Acknowledgments

This paper is supported by Zhejiang Province Basic Public Welfare Research Program (LGG19F020021), Shanghai Automotive Industry Science and Technology Development Foundation (1815).

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Authors and Affiliations

  1. School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Yundong Guo, Zhenyu Liu & Jianrong Tan

  2. College of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Xinjie Wang

  3. School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, 310027, China

    Hao Luo

  4. Pan Asia Technical Automotive Center Co., Ltd. (PATAC), Shanghai, 201206, China

    Huijie Pu

Authors
  1. Yundong Guo
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  2. Xinjie Wang
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  3. Hao Luo
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  4. Huijie Pu
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  5. Zhenyu Liu
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  6. Jianrong Tan
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Corresponding author

Correspondence to Hao Luo .

Editor information

Editors and Affiliations

  1. Institute of Intelligent Media Technology, Communication, University of Zhejiang, Hangzhou City, China

    Jie-Fang Zhang

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, China

    Chien-Ming Chen

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

  4. Department of Radio Communications and Video Technologies, Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Guo, Y., Wang, X., Luo, H., Pu, H., Liu, Z., Tan, J. (2022). Real-Time Multi-person Multi-camera Tracking Based on Improved Matching Cascade. In: Zhang, JF., Chen, CM., Chu, SC., Kountchev, R. (eds) Advances in Intelligent Systems and Computing. Smart Innovation, Systems and Technologies, vol 268. Springer, Singapore. https://doi.org/10.1007/978-981-16-8048-9_19

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