PERSON RE-IDENTIFICATION WITH DEEP DENSE FEATURE REPRESENTATION AND JOINT BAYESIAN

Person re-identification that aims at matching individuals across multiple camera views has become indispensable in intelligent video surveillance systems. It remains challenging due to the large variations of pose, illumination, occlusion and camera viewpoint. Feature representation and metric learning are the two fundamental components in person re identification. In this paper, we present a Special Dense Convolutional Neural Network (SD-CNN) to extract the feature and apply Joint Bayesian to measure the similarity of pedestrian image pairs. The SD-CNN can preserve more horizontal information to against viewpoint changes, maximize the feature reuse and ensure feature distributing discriminative. Joint Bayesian models the extracted feature representation as the sum of inter- and intra-personal variations, and the joint probability of two images being a same person can be obtained through log-likelihood ratio. Experiments show that our approach significantly outperforms state-of-the-art methods on several benchmarks of person re-identification.