Adaptive alignment network for person re-identification

Abstract

Person re-identification aims at identifying a target pedestrian across non-overlapping camera views. Pedestrian misalignment, which mainly arises from inaccurate person detection and pose variations, is a critical challenge for person re-identification. To address this, this paper proposes a new Adaptive Alignment Network (AAN), towards robust and accurate person re-identification. AAN automatically aligns pedestrian images from coarse to fine by learning both patch-wise and pixel-wise alignments, leading to effective pedestrian representation invariant to the variance of human pose and location across images. In particular, AAN consists of a patch alignment module, a pixel alignment module and a base network. The patch alignment module estimates the alignment offset for each image patch and performs patch-wise alignment with the offsets. The pixel alignment module is for fine-grained pixel-wise alignment. It learns the subtle local offset for each pixel and produces finely aligned feature map. Extensive experiments on three benchmarks, i.e., Market1501, DukeMTMC-reID and MSMT17 datasets, have demonstrated the effectiveness of the proposed approach.