Body-Structure Based Feature Representation For Person Re-Identification

Person re-identification is valuable for intelligent video surveillance and has drawn wide attention. Although person re-identification research is making progress, it still faces some challenges such as varying poses, illumination and viewpoints. As a major aspect of person re-identification, feature representation has been widely researched. Low-level descriptors are generally used in existing works, which do not take full advantage of body structure information and result in low discrimination. In this paper, body-structure based mid-level feature representation is proposed, which introduces body structure pyramid for codebook learning and feature pooling. Additionally, low computational LLC is used to encode mid-level features. Experimental results on two challenging datasets VIPeR and CUHK01 have demonstrated that our approach outperforms the state-of-the-art methods.