Decoupled Pose and Similarity Based Graph Neural Network for Video Person Re-Identification

Significant development of video person re-identification has been witnessed in recent years with deep learning technologies. Due to the complexity of human pose changes and the similarity between different individuals, learning discriminative features is still a challenging part of the video person re-identification task. To get rid of the effects of pose misalignment while keep the similarity of human appearance, in this paper, we propose a Pose and Similarity based Graph Neural Network in a decoupled manner, which consists of three independent branches to emphasize the respective roles of pose, local similarity and global similarity in the final descriptions. Compared to traditional Convolutional Neural Networks which tend to output similar global features in the case of highly similar pedestrians, the developed Graph Neural Networks are able to explore local semantic relationships between body parts, resulting in more discriminative features. To further eliminate the pose variation, we incorporate human skeleton information for feature map segmentation. Specifically, we propose to take a tree structure as the pose-aware adjacency graph of blocks in a person frame, which reveals the inherent connections within a human body. Experimental results on four widely used datasets demonstrate the effectiveness of our method.