Related Attention Network for Person Re-Identification

Abstract

Person Re-identification (ReID) is a critical technology in intelligent video surveillance. In practice, person ReID remains challenging due to pedestrian misalignment and background clutter. Pedestrian images are generated by manually cropping or pedestrian detection algorithms in most existing datasets, which mainly cause two drawbacks. On the one hand, detection errors may lead to pedestrian misalignment and cluttered background. On the other hand, hand-drawn bounding boxes are highly accurate but with inconsistent scales. In order to solve these problems, we make two contributions. Firstly, we design a simple and effective data pre-processing algorithm, which aligns pedestrian images into a standard template based on keypoints. Secondly, the Related Attention Network (RAN) is proposed to focus on human body regions by the pixel-level correlation, which improves ReID performance significantly. Experimental results on Market-1501 and DukeMTMC-reID datasets demonstrate the effectiveness of our method.