Person Search via Background and Foreground Contrastive Learning

The specific person search is the foundation of a wide range of applications in intelligent security and surveillance systems. Although detection and re-id have been widely studied, they are difficult to apply to practical applications directly. Therefore, this paper focuses on person search, which aims to solve person detection and person re-identification (re-id) jointly. The common practice is to append the standard detection loss and re-id branches parallelly on Faster RCNN. The traditional re-id utilized Online Instance Matching (OIM) to pull a sample closer to its identity class. However, the relationship among RoIs of an image has not been fully explored in previous methods. To address this issue, we propose Background and Foreground Contrastive Loss (BFCL) to further boost re-id performance. We consider that RoIs from one image have a high probability of containing similar patterns, which might disturb the re-id performance. Therefore, we proposed BFCL to strengthen the learning of distinguishing similar background and foreground by leveraging inter-RoIs pairwise similarity. In summary, our method jointly optimizes the regression loss, classification loss, re-id loss, and the proposed BFCL for achieving optimal performances in person search model. Experiments are performed on two large-scale person search datasets, CUHK-SYSU and PRW. Results show that the proposed BFCL consistently boosts the performance of the baseline framework SeqNet in two datasets. The improved results demonstrate the effectiveness of the proposed BFCL and the necessity of exploring the relationship among RoIs.