Comprehensive Samples Constrain for Person Search

Liangqi Li, Hua Yang, Lin Chen

Abstract: In this paper, we propose a method to further improve person search by fully utilizing the combination of pedestrian detection and person re-identification tasks. An improved constrain that utilizes comprehensive samples in the dataset is proposed to fully excavate information for recognition. Besides the label constrain for training the model in traditional classification task, unlabeled identities that do not have specific IDs are utilized as well to constitute a tailored triplet loss for more performance improvement. Meanwhile, a novel large-scale challenging dataset, SJTU318, which uses videos acquired through twelve cameras is proposed to demonstrate the effectiveness of our method. It contains 443 identities and 14,610 frames in which pedestrians are annotated with their bounding box positions and identities. Experiments conducted on a public dataset, CUHK-SYSU and our proposed dataset SJTU318 show that our method outperforms existing state-of-the-art approaches.

External IDs:dblp:conf/vcip/LiYC18