Person re-identification via rich color-gradient feature
Person re-identification refers to match the same pedestrian across disjoint views in non-overlapping camera networks. Lots of local and global features in the literature are put forward to solve the matching problem, where color feature is robust to viewpoint variance and gradient feature provides a rich representation robust to illumination change. However, how to effectively combine the color and gradient features is an open problem. In this paper, to effectively leverage the color-gradient property in multiple color spaces, we propose a novel Second Order Histogram feature (SOH) for person reidentification in large surveillance dataset. Firstly, we utilize discrete encoding to transform commonly used color space into Encoding Color Space (ECS), and calculate the statistical gradient features on each color channel. Then, a second order statistical distribution is calculated on each cell map with a spatial partition. In this way, the proposed SOH feature effectively leverages the statistical property of gradient and color as well as reduces the redundant information. Finally, a metric learned by KISSME [1] with Mahalanobis distance is used for person matching. Experimental results on three public datasets, VIPeR, CAVIAR and CUHK01, show the promise of the proposed approach.