Weakly Supervised Pedestrian Segmentation for Person Re-Identification

Person re-identification (RelD) is an important problem in intelligent surveillance and public security. Among all the solutions to this problem, existing mask-based methods first use a well-pretrained segmentation model to generate a foreground mask, in order to exclude the background from ReID. Then they perform the RelD task directly on the segmented pedestrian image. However, such a process requires extra datasets with pixel-level semantic labels. In this paper, we propose a Weakly Supervised Pedestrian Segmentation (WSPS) framework to produce the foreground mask directly from the RelD datasets. In contrast, our WSPS only requires image-level subject ID labels. To better utilize the pedestrian mask, we also propose the Image Synthesis Augmentation (ISA) technique to further augment the dataset. Experiments show that the features learned from our proposed framework are robust and discriminative. Compared with the baseline, the mAP of our framework is about 4.4%, 11.7%, and 4.0% higher on three widely used datasets including Market-1501, CUHK03, and MSMT17. The code will be available soon.

Automated summary

In this paper, a weakly supervised pedestrian segmentation framework is proposed to directly generate the foreground mask from person re-identification datasets with only image-level subject ID labels. The Image Synthesis Augmentation (ISA) technique is also introduced to further enhance the dataset. Experimental results demonstrate that the proposed framework learns robust and discriminative features, achieving significant improvement in mAP compared to the baseline on widely used datasets including Market-1501, CUHK03, and MSMT17. The code will be made available soon.