Abstract
Person re-identification in a surveillance video is a challenging task because of wide variations in illumination, viewpoint, pose, and occlusion. In this paper, from feature representation and metric learning perspectives, we design a robust color invariant model for person re-identification. Firstly, we propose a novel feature representation called Color Invariant Feature (CIF), it is robust to illumination and viewpoint changes. Secondly, to learn a more discriminant metric for matching persons, XQDA metric learning algorithm is improved by adding a clustering step before computing metric, the new metric learning method is called Multiple Cross-view Quadratic Discriminant Analysis (MXQDA). Experiments on two challenging person re-identification datasets, VIPeR and CUHK1, show that our proposed approach outperforms the state of the art.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. This work is partially supported by China National Natural Science Foundation under grant No. 61203247, 61573259 and 61573255. It is also supported by the Fundamental Research Funds for the Central Universities (Grant No. 2013KJ010). It is also partially supported by Changzhou Key Laboratory of Cloud Computing and Intelligent Information Processing grant No. CM20123004-KF01 and by the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education under grant No. 30920130122005. It is also partially supported by the program of Further Accelerating the Development of Chinese Medicine Three Year Action of Shanghai grant No. ZY3-CCCX-3-6002.
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Chen, Y., Zhao, C., Wang, X., Gao, C. (2016). Robust Color Invariant Model for Person Re-Identification. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_76
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DOI: https://doi.org/10.1007/978-3-319-46654-5_76
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