Pose-Guided Part-Based Adaptive Pyramid Features for Occluded Person Reidentification

Reidentifying an occluded person across nonoverlapping cameras is still a challenging task. In this work, we propose a novel pose-guided part-based adaptive pyramid neural network for occluded person reidentification. Firstly, to alleviate the impact of occlusion, we utilize pose landmarks to generate pose-guided attention maps. The attention maps will help the model focus on the nonoccluded regions. Secondly, we use pyramid pooling to extract multiscale features in order to address the scale variation problem. The generated pyramid features are then multiplied by attention maps to achieve pose-guided adaptive pyramid features. Thirdly, we propose a pose-guided body part partition scheme to deal with the alignment problem. Accordingly, the adaptive pyramid features are divided into partitions and fed into individual fully connected layers. In the end, all the part-based matching scores are fused with a weighted sum rule for person reidentification. The effectiveness of our method is clearly validated by the experimental results on two popular occluded and holistic datasets, i.e., Occluded-DukeMTMC and the Market-1501.