Sequences consistency feature learning for video‐based person re‐identification
Kai Zhao, Deqiang Cheng, Qiqi Kou, Jiahan Li, Ruihang Liu · Electronics Letters · 2021
Abstract 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.