Improving Person ReIdentification Performance Using Body Mask Via CrossLearning Strategy The task of person reidentification (reid) is to find the same pedestrian across nonoverlapping cameras. Normally, the performance of person reid can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a crosslearning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two stateoftheart person reid methods to verify the effectiveness of our proposed crosslearning strategy. Our experiments are carried out on two publicly available person reid datasets. Obvious performance improvements can be observed on both datasets.

DomainCamera Adaptation for Unsupervised Person ReIdentification Although supervised person reidentification (ReID) performance has been significantly improved in recent years, it is still a challenge for unsupervised person ReIddue to its absence of labels across disjoint camera views. On the other hand, ReIdmodels trained on source domain usually offer poor performance when they are tested on target domain due to interdomain bias e.g. different classes and intradomain difference e.g camera variance. To overcome this problem, given a labeled source training domain and an unlabeled target training domain, we propose an unsupervised transfer method, DomainCamera Adaptation model, to generate a pseudo target domain by bridging interdomain bias and intradomain difference. The idea is to fill the absence of labels in target domain by transferring labeled images of source domain to target domain across cameras. Then we propose a crossdomain classification loss to extract discriminative representation across domains. The intuition is to think of unsupervised learning as semisupervised learning in target domain. We evaluate our deep model on Market1501 and DukeMTMCreID and the results show our model outperforms the stateofart unsupervised ReID methods by large margins.