Saliencybased Deep Multilevel Semantic Feature Fusion for Person Reidentification Person reidentification (ReID) is a challenging problem due to external environmental disturbances and significant intraclass appearance variations. Discriminative person features should cover global and partial representations, and accordingly can describe highlevel and middlelevel semantic information of persons. To achieve this goal, a saliencybased deep multilevel semantic feature representation and fusion algorithm is proposed. Firstly, a feature fusion scheme at middle layer of our deep network is presented to effectively fuse global CNN features. Secondly, a partbased feature extraction method is designed to extract highlevel semantic features. In addition, a parameterfree multiscale saliencybased enhancement algorithm is proposed to compute patchlevel saliency scores to enhance the distinctiveness of partbased features. Experimental results on three public datasets, namely, Market1501, DukeMTMCreID, and CHUK03, demonstrate the effectiveness of the proposed method compared with stateoftheart ReID approaches.

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.