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People Re-identification Based on Bags of Semantic Features

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9010))

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Abstract

People re-identification has attracted a lot of attention recently. As an important part in disjoint cameras based surveillance system, it faces many problems. Various factors like illumination condition, viewpoint of cameras and occlusion make people re-identification a difficult task. In this paper, we exploit the performance of bags of semantic features on people re-identification. Semantic features are mid-level features that can be directly described by words, such as hair length, skin tone, race, clothes colors and so on. Although semantic features are not as discriminative as local features used in existing methods, they are more invariant. Therefore, good performance on people re-identification can be expected by combining a set of semantic features. Experiments are carried out on VIPeR dataset. Comparison with some state-of-the-art works is provided and the proposed method shows better performance.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Zhi Zhou & Eam Khwang Teoh

  2. Visual Computing Department, Institute for Infocomm Research (I2R), Singapore, 138632, Singapore

    Yue Wang

Authors
  1. Zhi Zhou
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  2. Yue Wang
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  3. Eam Khwang Teoh
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Corresponding author

Correspondence to Zhi Zhou .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C. V. Jawahar

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Zhou, Z., Wang, Y., Teoh, E.K. (2015). People Re-identification Based on Bags of Semantic Features. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_42

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  • DOI: https://doi.org/10.1007/978-3-319-16634-6_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16633-9

  • Online ISBN: 978-3-319-16634-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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