Pose-Guided Spatial Alignment and Key Frame Selection for One-Shot Video-Based Person Re-Identification

• Main Authors: Yuzhong Chen, Tengda Huang, Yuzhen Niu, Xiao Ke, Yangyang Lin
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Person re-identification; one-shot learning; spatial alignment; key frame selection; frame feature pooling
• Online Access: https://ieeexplore.ieee.org/document/8736229/

One-shot video-based person re-identification exploits the unlabeled data by using a single-labeled sample for each individual to train a model and to reduce the need for laborious labeling. Although recent works focusing on this task have made some achievements, most state-of-the-art models are vulnerable to misalignment, pose variation and corrupted frames. To address these challenges, we propose a one-shot video-based person re-identification model based on pose-guided spatial alignment and KFS. First, a spatial transformer sub-network trained using pose-guided regression is employed to perform the spatial alignment. Second, we propose a novel training strategy based on KFS. Key frames with abruptly changing poses are deliberately identified and selected to make the network adaptive to pose variation. Finally, we propose a frame feature pooling method by incorporating long short-term memory with an attention mechanism to reduce the influence of corrupted frames. Comprehensive experiments are presented based on the MARS and DukeMTMC-VideoReID datasets. The mAP values for these datasets reach 46.5% and 68.4%, respectively, demonstrating that the proposed model achieves significant improvements over state-of-the-art one-shot person re-identification methods.