Cross-scenario Transfer Person Re-identification

Person re-identification is to match images of the same person captured in disjoint camera views and at different time. In order to obtain a reliable similarity measurement between images, manually annotating a large amount of pairwise cross-camera-view person images is deemed necessary. However, such a kind of annotation is both costly and impractical for efficiently deploying a re-identification system to a completely new scenario, a new setting of non-overlapping camera views between which person images are to be matched. To solve this problem, we consider utilizing other existing person images captured in other scenarios to help the re-identification system in a target (new) scenario, provided that a few samples are captured under the new scenario. More specifically, we tackle this problem by jointly learning the similarity measurements for re-identification in different scenarios in an asymmetric way. To model the joint learning, we consider that the re-identification models share certain component across tasks. A distinct consideration in our multi-task modeling is to extract the discriminant shared component that reduces the cross-task data overlap in the shared latent space during the joint learning, so as to enhance the target inter-class separation in the shared latent space. For this purpose, we propose to maximize the cross-task data discrepancy (CTDD) on the shared component during asymmetric multi-task learning, along with maximizing the local inter-class variation and minimizing local intra-class variation on all tasks. We call our proposed method the constrained asymmetric multi-task discriminant component analysis (cAMT-DCA). We show that cAMT-DCA can be solved by a simple eigen-decomposition with a closed form, getting rid of any iterative learning used in most conventional multi-task learning. The experimental results show that the proposed transfer model gains a clear improvement against the related non-transfer and general multi-task person re-- dentification models.