Person re-Identification with Gradual Background Suppression

Person re-identification plays an important role in public security. However, owing to the interference of background clutters, its performance still needs to be improved. Several mask-based methods aim to solve this problem by totally removing the background clutters, but the promotion is limited because of the mask sharpening effect. In this paper, we propose a novel person re-identification method with Gradual Background Suppression (GBS). The GBS adopts several CNN branches to extract deep features of images with different weight distributions between background and human body. Thus, it can not only reduce the background clutters but also keep the smoothness of target pedestrians. Afterwards, deep features from different CNN branches are integrated with a fusion scheme, and the fused feature is capable of balancing the influence of background clutter and mask sharpening. Extensive experiments have been conducted and the results prove the superiority of the proposed GBS over the background removal approach. Comparing with the state-of-the-art methods, our method achieves remarkable performance with 6.6%, 7.58% and 8.26% improvement of mAP on dataset Market-1501, CUHK03-labeled and CUHK03-detected, respectively.