Kernel cross-view collaborative representation based classification for person re-identification

Person re-identification aims at the maintenance of a global identity as a person moves among non-overlapping surveillance cameras. It is a hard task due to different illumination conditions, viewpoints and the small number of annotated individuals from each pair of cameras (small-sample-size problem). Common subspace learning methods have been proposed to handle the camera transition problems. However, after learning the low-dimensional representation, these methods usually compute distances using a simple cosine or Mahalanobis distance. Therefore, an still open question is how to better match probe and gallery images in the learned common subspace considering reduced number of training samples and the nonlinear behavior of the data. Collaborative Representation based Classification (CRC) has been employed successfully to address the small-sample-size problem in computer vision. However, the original CRC formulation is not well-suited for person re-identification since it does not consider that probe and gallery samples are from different cameras. Furthermore, it is a linear model, while appearance changes caused by different camera conditions indicate a strong nonlinear transition between cameras. To overcome such limitations, we propose the Kernel Cross-View Collaborative Representation based Classification (Kernel X-CRC), method that represents probe and gallery images by balancing representativeness and similarity nonlinearly. According to experimental results, we achieve state-of-the-art for rank-I matching rates in three person re-identification datasets (CUHK03, PRID450S and GRID) and the second best results on VIPeR and CUHK01 datasets. Furthermore, we present outperforming results on Market-1501 dataset demonstrating that the Kernel X-CRC is suitable to a large-scale and multiple cameras scenario. (C) 2018 Elsevier Inc. All rights reserved.