Key Parts Spatio-Temporal Learning for Video Person Re-identification.

Person re-identification (Re-ID) is a technology to identify specific pedestrians in different scenarios. In recent years, Re-ID has been widely used in surveillance, supermarket and smart city. However, there are still many challenges in this field, including complex background, pose changes, and occlusion dislocation. We propose a novel Key Parts Spatio-temporal Learning (KSTL) framework to alleviate the above problems. Specifically, we first use the mask method based on keypoint detection to locate and extract the key part features of the human body. Then, we introduce Spatio-temporal Learning (STL) block based on key parts to realize the mutual transfer and learning of key parts features of multiple frames. Finally, we fuse the learned key part features and global features as the final video representation. The method we propose can not only accurately learn the features of key parts, but also make full use of the timing information in the video, thus achieving good detection results. We conduct extensive experiments on three public benchmarks, and the results demonstrate the effectiveness and superiority of KSTL.