Unsupervised Joint Subspace and Dictionary Learning for Enhanced Cross-Domain Person Re-Identification

Person re-identification (Re-ID) has drawn increasing attention from both academia and industry due to its great potentials in surveillance applications. Most existing research efforts have attempted to tackle cross-view variation in single-domain person Re-ID. However, there is still a lack of effective approaches to cross-domain person Re-ID problem. In this paper, an unsupervised joint subspace and dictionary learning (UJSDL) framework is proposed to address the cross-domain person Re-ID problem, where both cross-view (i.e., across different cameras in the same network of cameras) and cross-domain (i.e., across different network of cameras) variation are jointly addressed. In particular, to reduce the impact of cross-view distribution variation, the graph Laplacian approach is used to project the images from different camera views in each domain into a shared subspace. To alleviate the impact of cross-domain distribution variation, a shared dictionary is learned from all the projection subspaces such that the discriminative information from both the labeled source datasets and the unlabeled target dataset are well encoded. To efficiently solve the joint subspace and dictionary learning task, an alternating optimization algorithm is presented. We used multiple different feature sets and conducted experiments on multiple benchmark datasets as the target domain. The results demonstrate that UJSDL outperforms the state-of-the-art approaches.