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Public Security Video and Image Analysis Challenge: A Retrospective

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

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Abstract

The Public Security Video and Image Analysis Challenge (PSVIAC) is a benchmark in object detection and instance search on public security surveillance videos. This challenge is first held in 2016, attracting participation from more than twenty institutions. This paper provides a review of this challenge, including tasks definition, datasets creation, ground truth annotation, and results comparison and analysis. We conclude the paper with some future improvements.

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Acknowledgement

Our research was sponsored by following projects: Program of Science and Technology Commission of Shanghai Municipality (No. 15530701300, 15XD1520200, 14DZ2252900); 2012 IoT Program of Ministry of Industry and Information Technology of China; Key Project of the Ministry of Public Security (No. 2014JSYJA007); Shanghai Science and Technology Innovation Action Plan (No. 16511101700).

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Authors and Affiliations

  1. The Third Research Institute of the Ministry of Public Security, Shanghai, 200031, China

    Gengjian Xue, Wenfei Wang, Jie Shao, Chen Liang, Jinjing Wu, Hui Yang, Xiaoteng Zhang, Lin Mei & Chuanping Hu

Authors
  1. Gengjian Xue
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  2. Wenfei Wang
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  3. Jie Shao
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  4. Chen Liang
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  5. Jinjing Wu
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  6. Hui Yang
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  7. Xiaoteng Zhang
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  8. Lin Mei
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  9. Chuanping Hu
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Corresponding author

Correspondence to Gengjian Xue .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Xue, G. et al. (2017). Public Security Video and Image Analysis Challenge: A Retrospective. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_32

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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