Due to illumination changes, partial occlusions, and object scale differences, person re-identification over disjoint camera views becomes a challenging problem. To address this problem, a variety of image representations have been put forward. In this paper, the illumination invariance and distinctiveness of different color models including the proposed color model are firstly evaluated. Since color distribution is robust to image scales and partial occlusions, color distributions based on different color models are then calculated and fused in the stage of feature extraction. Different color models obtain robustness to different types of illumination and thus fusing them can compensate each other and contribute to better performance. In the stage of feature matching, a weighted KISSME is presented to learn a better distance metric than the original KISSME. Experimental results demonstrate its feasibility and effectiveness. Finally, image pairs are matched based on the learned distance metric. Experiments conducted on two public benchmark datasets (VIPeR and PRID 450S) show that the proposed algorithm outperforms the state-of-the-art methods.
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Color Models and Weighted Covariance Estimation for Person Re-identification
Semantic Scholar · Computer Science · 2014
Abstract
Due to illumination changes, partial occlusions, and object scale differences, person re-identification over disjoint camera views becomes a challenging problem. To address this problem, a variety of image representations have been put forward. In this paper, the illumination invariance and distinctiveness of different color models including the proposed color model are firstly evaluated. Since color distribution is robust to image scales and partial occlusions, color distributions based on different color models are then calculated and fused in the stage of feature extraction. Different color models obtain robustness to different types of illumination and thus fusing them can compensate each other and contribute to better performance. In the stage of feature matching, a weighted KISSME is presented to learn a better distance metric than the original KISSME. Experimental results demonstrate its feasibility and effectiveness. Finally, image pairs are matched based on the learned distance metric. Experiments conducted on two public benchmark datasets (VIPeR and PRID 450S) show that the proposed algorithm outperforms the state-of-the-art methods.