Sequences consistency feature learning for video-based person re-identification

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping cameras. A major challenge of the person re-identification is the serious intra-class distance caused by cropped frames variation in video sequences. To address this issue, a novel sequences consistency feature learning (SCFL) framework for video-based person re-identification is proposed. Specifically, SCFL utilizes a deep neural network and the proposed sequences consistency loss to learn sequences-invariant features for each pedestrian, which decreases the intra-class distance across the partial occlusions, inaccurate detection and viewpoint variation. During the training process, SCFL slices the video-level features and computes the cosine similarity between disjoint sequences features pairs of the same pedestrian as the sequences consistency loss to minimize the intra-class distance. The experiments demonstrate that our approach improves the performance of the existing deep networks and achieves competitive performance on the large-scale benchmark datasets including MARS and DukeMTMC-VideoReID.