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A half-precision compressive sensing framework for end-to-end person re-identification

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Abstract

Compressive sensing (CS) approaches are useful for end-to-end person re-identification (Re-ID) in reducing the overheads of transmitting and storing video frames in distributed multi-camera systems. However, the reconstruction quality degrades appreciably as the measurement rate decreases for existing CS methods. To address this problem, we propose a half-precision CS framework for end-to-end person Re-ID named HCS4ReID, which efficiently recoveries detailed features of the person-of-interest regions in video frames. HCS4ReID supports half-precision CS sampling, transmitting and storing CS measurements with half-precision floats, and CS reconstruction with two measurement rates. Extensive experiments implemented on the PRW dataset indicate that the proposed HCS4ReID achieves 1.55 \(\times\) speedups over the single-precision counterpart on average for the CS sampling on an Intel HD Graphics 530, and only half-network bandwidth and storage space are needed to transmit and store the generated CS measurements. Comprehensive evaluations demonstrate that the proposed HCS4ReID is a scalable and portable CS framework with two measurement rates, and suitable for end-to-end person Re-ID. Especially, it achieves the comparable performance on the reconstructed PRW dataset against CS reconstruction with single-precision floats and a single measurement rate.

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Acknowledgements

The research was partially funded by the Program of National Natural Science Foundation of China (Grant No. 61751204), the National Outstanding Youth Science Program of National Natural Science Foundation of China (Grant No. 61625202), the International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China (Grant No. 61661146006), the National Key R&D Program of China (Grant Nos. 2016YFB0201303, 2016YFB0200201), the National Natural Science Foundation of China (Grant Nos. 61772182, 61802032), Science and Technology Plan of Changsha (K1705032). The authors would like to thank Tianming Jin for his help in improving the paper.

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Authors and Affiliations

  1. College of Computer, National University of Defense Technology, Changsha, 410073, China

    Longlong Liao & Jie Liu

  2. State Key Laboratory of High Performance Computing, Changsha, 410073, China

    Longlong Liao

  3. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410022, China

    Zhibang Yang

  4. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Qing Liao

  5. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Kenli Li

  6. Department of Computer Science, State University of New York, New Paltz, NY, 12561, USA

    Keqin Li

  7. Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, 78249, USA

    Qi Tian

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Liao, L., Yang, Z., Liao, Q. et al. A half-precision compressive sensing framework for end-to-end person re-identification. Neural Comput & Applic 32, 1141–1155 (2020). https://doi.org/10.1007/s00521-019-04424-1

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