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Sparse coding with cross-view invariant dictionaries for person re-identification

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Abstract

The task of matching observations of the same person in disjoint views captured by non-overlapping cameras is known as the person re-identification problem. It is challenging owing to low-quality images, inter-object occlusions, and variations in illumination, viewpoints and poses. Unlike previous approaches that learn Mahalanobis-like distance metrics, we propose a novel approach based on dictionary learning that takes the advances of sparse coding of discriminatingly and cross-view invariantly encoding features representing different people. Firstly, we propose a robust and discriminative feature extraction method of different feature levels. The feature representations are projected to a lower computation common subspace. Secondly, we learn a single cross-view invariant dictionary for each feature level for different camera views and a fusion strategy is utilized to generate the final matching results. Experimental statistics show the superior performance of our approach by comparing with state-of-the-art methods on two publicly available benchmark datasets VIPeR and PRID 2011.

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Acknowledgements

This work has been supported by New Century Excellent Talents in University of Ministry of Education under Grant NCET-12-0358, and Program of Shanghai Technology Research Leader under Grant 16XD1424400.

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Authors and Affiliations

  1. Department of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yunlu Xu, Jie Guo, Zheng Huang & Weidong Qiu

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  1. Yunlu Xu
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  2. Jie Guo
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  3. Zheng Huang
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  4. Weidong Qiu
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Correspondence to Jie Guo.

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Xu, Y., Guo, J., Huang, Z. et al. Sparse coding with cross-view invariant dictionaries for person re-identification. Multimed Tools Appl 77, 10715–10732 (2018). https://doi.org/10.1007/s11042-017-4893-5

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