Instance Enhancing Loss: Deep Identity-Sensitive Feature Embedding for Person Search

Person search, which is vital for intelligent surveillance, aims at detecting and re-identifying pedestrians from whole monitoring images. However, due to the inaccurate pedestrian detections and extremely few instances per training identity, it remains challenging to learn discriminative representations only by labeled identities for person search. To this end, this paper proposes a novel loss function called instance enhancing loss (IEL) to learn deep identity-sensitive features by introducing unlabeled identity information. Specifically, the proposed IEL can selectively annotate unlabeled identities with similar appearances to labeled identities, and utilize these unlabeled identities in conjunction with labeled identities to train the person search network. The amount of unlabeled identities used as labeled instances can be quantitatively adjusted. Moreover, the proposed IEL is trainable and easy to optimize by back propagation algorithms. Extensive experiments on two benchmark datasets, namely CUHK-SYSU and PRW, show that our method outperforms state-of-the-arts for person search.