Instance Guided Proposal Network for Person Search

Person detection networks have been widely used in person search. These detectors discriminate persons from the background and generate proposals of all the persons from a gallery of scene images for each query. However, such a large number of proposals have a negative influence on the following identity matching process because many distractors are involved. In this paper, we propose a new detection network for person search, named Instance Guided Proposal Network (IGPN), which can learn the similarity between query persons and proposals. Thus, we can decrease proposals according to the similarity scores. To incorporate information of the query into the detection network, we introduce the Siamese region proposal network to Faster-RCNN and we propose improved cross-correlation layers to alleviate the imbalance of parameters distribution. Furthermore, we design a local relation block and a global relation branch to leverage the proposal-proposal relations and query-scene relations, respectively. Extensive experiments show that our method improves the person search performance through decreasing proposals and achieves competitive performance on two large person search benchmark datasets, CUHK-SYSU and PRW.