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Sparse representations based distributed attribute learning for person re-identification

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Abstract

Searching for specific persons from surveillance videos captured by different cameras, known as person re-identification, is a key yet under-addressed challenge. Difficulties arise from the large variations of human appearance in different poses, and from the different camera views that may be involved, making low-level descriptor representation unreliable. In this paper, we propose a novel Sparse Representations based Distributed Attribute Learning Model (SRDAL) to encode targets into semantic topics. Compared to other models such as ELF, our model performs best by imposing semantic restrictions onto the generation of human specific attributes and employing Deep Convolutional Neural Network to generate features without supervision for attributes learning model. Experimental results show that our method achieves state-of-the-art performance.

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Acknowledgements

This research is supported by the Natural Science Foundation of China No.61602215, 61672268, the science foundation of Jiangsu province No.BK20150527, No. BE2015137, the science foundation of Zhenjiang city No.SH2014017, the scientific research funds for senior talents of Jiangsu University No.15JDG180, China State Scholarship Fund No.201608320098 and International Postdoctoral Exchange Fellowship Program No.201653.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China

    Keyang Cheng, Kaifa Hui & Yongzhao Zhan

  2. School of Engineering and Design, Brunel University, Uxbridge, London, UB8 3PH, UK

    Maozhen Li

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  1. Keyang Cheng
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  2. Kaifa Hui
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  3. Yongzhao Zhan
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  4. Maozhen Li
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Correspondence to Keyang Cheng.

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Cheng, K., Hui, K., Zhan, Y. et al. Sparse representations based distributed attribute learning for person re-identification. Multimed Tools Appl 76, 25015–25037 (2017). https://doi.org/10.1007/s11042-017-4967-4

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