Cross-domain person re-identification with pose-invariant feature decomposition and hypergraph structure alignment

Abstract

Person Re-identification (Re-ID) has attracted more and more attention thanks to its great practical value in the field of video surveillance. Most works have focused on solving the problem of supervised Re-ID on a single domain and made significant progress. However, the cross-domain Re-ID is still challenging due to the domain bias between the source and target domains. To this end, we propose a dictionary learning algorithm based on matrix factorization to eliminate the influence of style and pedestrian pose information on the cross-domain Re-ID. Specifically, the proposed approach includes two novel parts: (1) the original visual feature is decomposed into pose-invariant feature space, camera-style feature space and residual feature space to extract discriminant pose-invariant feature that is not affected by style and pedestrian pose information, such that the influence of interference information between pedestrians on recognition can be eliminated; (2) considering the domain-invariance of attribute, a hypergraph structure alignment is introduced to integrate pose-invariant feature, attribute and pedestrian identity into a dictionary learning framework. The relationship between pose-invariant feature and attribute is built so that the pedestrian attribute of the target dataset can be accurately predicted during testing. Finally, the pedestrian similarity measurement can be carried out by combining the pose-invariant feature and attribute of pedestrians. The effectiveness of the proposed algorithm is verified with the experiments on several benchmark Re-ID datasets.