Relation-based global-partial feature learning network for video-based person re-identification

Abstract

Video-based person re-identification (Re-ID) aims to match the same pedestrian from the video sequences captured by non-overlapping cameras. It is the key to fully extracting abundant spatial and temporal information from the video frames in video-based Re-ID. In this paper, a novel Relation-Based Global-Partial Feature Learning framework is proposed to explore discriminative spatiotemporal features with the help of the global and partial relationship between frames. Specifically, we propose a Relation-Based Global Feature Learning Module (RGL) to obtain global references for generating features correlation maps between frames in the video sequence and determine the importance of frame-level features. As the supplementary of the global relation-based features, a Relation-Based Partial Feature Learning Module (RPL) is also proposed to obtain the relationship between partial features of the same spatial position in different frames to enhance the frame-level partial representation. Moreover, we design a multi-level training scheme to deeply supervise our model. Extensive experiments are conducted on three public video-based person Re-ID datasets, and the results indicate that our framework achieves state-of-the-art performance on three benchmarks.