Improved Model Structure with Cosine Margin OIM Loss for End-to-End Person Search

End-to-end person search is a novel task that integrates pedestrian detection and person re-identification (re-ID) into a joint optimization framework. However, the pedestrian features learned by most existing methods are not discriminative enough due to the potential adverse interaction between detection and re-ID tasks and the lack of discriminative power of re-ID loss. To this end, we propose an Improved Model Structure (IMS) with a novel re-ID loss function called Cosine Margin Online Instance Matching (CM-OIM) loss. Firstly, we design a model structure more suitable for person search, which alleviates the adverse interaction between the detection and re-ID parts by reasonably decreasing the network layers shared by them. Then, we conduct a full investigation of the weight of re-ID loss, which we argue plays an important role in end-to-end person search models. Finally, we improve the Online Instance Matching (OIM) loss by adopting a more robust online update strategy, and importing a cosine margin into it to increase the intra-class compactness of the features learned. Extensive experiments on two challenging datasets CUHK-SYSU and PRW demonstrate our approach outperforms the state-of-the-arts.