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Online learning of dynamic multi-view gallery for person Re-identification

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Abstract

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.

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Acknowledgments

This research has been partially supported by the grants of China 973 project 2011CB302203, NSFC 61375019 and NSFC 61273285.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Yanna Zhao

  2. School of Automation, Shanghai Jiao Tong University, Shanghai, China

    Yanna Zhao, Xu Zhao, Zongjie Xiang & Yuncai Liu

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  1. Yanna Zhao
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  2. Xu Zhao
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  3. Zongjie Xiang
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  4. Yuncai Liu
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Correspondence to Yanna Zhao or Xu Zhao.

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Zhao, Y., Zhao, X., Xiang, Z. et al. Online learning of dynamic multi-view gallery for person Re-identification. Multimed Tools Appl 76, 217–241 (2017). https://doi.org/10.1007/s11042-015-3015-5

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  • DOI: https://doi.org/10.1007/s11042-015-3015-5

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