A Discriminatively Learned Feature Embedding Based on Multi-Loss Fusion For Person Search

Abstract

Person search is a challenging task that requires to address pedestrian detection and person re- identification simultaneously. Though significant progress has been made in detection and re-identification respectively, the similar appearances of persons, pedestrian misdetections and false alarms still have adverse effects on person search. To this end, an improved end-to-end person search network with multi -loss is proposed to jointly optimize detection and re-identification. Firstly, a pre-trained network is designed to obtain proper initial state for the whole training network. Then, to enhance the person search model, an improved online instance matching (IOIM) loss is proposed by hardening the distribution of labeled identities and softening the distribution of unlabeled identities. Finally, considering the intra-class compactness of features learned by center loss, the IOIM loss is combined with center loss by the proposed multi-loss fusion strategy, which can learn more discriminative feature embeddings. Experimental results on two challenging datasets CUHK-SYSU and PRW demonstrate our approach significantly outperforms the state-of-the-arts.