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Video-Based Person Re-identification by Region Quality Estimation and Attributes

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Cognitive Systems and Signal Processing (ICCSIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1005))

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Abstract

Person re-identification (re-ID) has become a significant area in automated video surveillance. The main challenge of this task lies in changes of pedestrians’ appearance across different cameras due to occlusions and illumination variations. Video-based person re-ID provides more information about pedestrians, but how to aggregate useful information of all frames is still an open issue. Although using region quality estimation network (RQEN) can achieve relative good performance on standard datasets, it is limited by the alignment of all bounding boxes. If the position of person varies in bounding boxes, it will influence the region generating unit, which thus decreases the accuracy of re-ID. To solve this limitation, this paper proposes a network combining the quality estimation and attribute classification. While convolution neural network can effectively learn global features, attribute recognition focus more on details. Therefore, our method achieves comparable results on iLIDS-VID and PRID 2011 datasets with the help of attribute classification.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Room A407 Aerospace Building, 800 Dongchuan Road, Shanghai, 200240, People’s Republic of China

    Simin Xu & Shiqiang Hu

Authors
  1. Simin Xu
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  2. Shiqiang Hu
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Correspondence to Simin Xu .

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Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan, China

    Dewen Hu

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Xu, S., Hu, S. (2019). Video-Based Person Re-identification by Region Quality Estimation and Attributes. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-13-7983-3_6

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  • DOI: https://doi.org/10.1007/978-981-13-7983-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7982-6

  • Online ISBN: 978-981-13-7983-3

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