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Efficient lightweight video person re-identification with online difference discrimination module

  • 1182: Deep Processing of Multimedia Data
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Abstract

Video person re-identification (video Re-ID) is a key technology applied to video surveillance and security. Typical person re-identification is designed to retrieve the correct match of the target image (query) from gallery images, while video Re-ID extends this to query from gallery videos. The main factors affecting the video Re-ID model are: (i) a high-quality frame-level feature extractor, and (ii) temporal modeling that combines frame-level features into a feature for retrieval. In this work, we use ShuffleNet V2-based lightweight algorithm for video Re-ID, which can meet the demand for practical application and solve the problem of high consumption for computing resources, and maintain high performance. At the same time, the lightweight space attention mechanism Spatial Group-wise Enhance (SGE) module is used to view the person in more detail, which makes the feature representation more compact and effectively improves the retrieval accuracy. Finally, we design an Online Difference Discrimination (ODD) module to measure the feature gap between video frames, and use this module to make different temporal modeling for different quality video sequences. Experiments on three datasets (i.e., iLIDS-VID, PRID2011 and MARS) show that our method is competitive with state-of-the-art methods.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (No. 61772530, No. 61806206, No. 61876121), in part by the State’s Key Project of Research and Development Plan of China (No.2016YFC0600908), in part by the Natural Science Foundation of Jiangsu Province of China (No. BK20171192, No. BK20180639), in part by the Six Talent Peaks Project in Jiangsu Province (No. 2018-XYDXX-044), in part by the Open Foundation of the Suzhou Smart City Research Institute, Suzhou University of Science and Technology (No. SZSCR2019005), and in part by the project supported by Xuzhou Science and Technology Plan Funds (No. KC19005).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Cunyuan Gao, Rui Yao, Yong Zhou, Jiaqi Zhao & Liang Fang

  2. The Suzhou Smart City Research Institute, Suzhou University of Science and Technology, Suzhou, 215009, China

    Rui Yao & Fuyuan Hu

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  1. Cunyuan Gao
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  2. Rui Yao
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Correspondence to Rui Yao.

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Gao, C., Yao, R., Zhou, Y. et al. Efficient lightweight video person re-identification with online difference discrimination module. Multimed Tools Appl 81, 19169–19181 (2022). https://doi.org/10.1007/s11042-021-10543-6

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  • DOI: https://doi.org/10.1007/s11042-021-10543-6

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