Abstract
Person re-identification has attracted increasing interests due to its broad application in automatic search and video surveillance. It is easy for humans to recognize person identities but difficult for computers. Thus, knowing how humans to recognize person identities is helpful to improve the performance of the computers person re-identification. In this paper, we propose an effective feature representation based on salient regions and a pool of multiple metric learning for person re-identification. The proposed feature representation extracts local details (salience regions) and global distribution of pedestrian images. To reduce the effects of illumination changes, we apply probability histogram in four kinds of color spaces where similar color can be characterized by the similar histogram distribution to different color spaces. Moreover, a pool of multiple metric learning is applied to all features captured from different spaces and models. The proposed method has been evaluated on two public datasets. Experimental results show that the proposed method outperforms others.
Z. Zhang and C. Zhao—Authors contributed equally.
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Zhang, Z., Zhao, C., Miao, D., Wang, X., Lai, Z., Yang, J. (2017). Saliency-Based Person Re-identification by Probability Histogram. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_24
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