End-to-end feature diversity person search with rank constraint of cross-class matrix

Person search aims to locate and identify specific persons from a series of uncropped images, which has achieved a significant impact on many human-related applications, e.g., person activity understanding and person tracking. Person search includes two sub-tasks: person detection and person re-identification. Person detection focuses on finding the commonality of all identities, while person re-identification focuses on finding the differences among different identities. To mitigate the impact of the different purposes of these two sub-tasks on a person search model, we split the ResNet50 network according to the sub-task, and propose a feature diversity person search (FDPS) framework based on the rank constraint of the cross-class matrix. We first construct a model called the split-baseline (S-bsl), and then introduce the deformable convolution to locate the entire person area. More importantly, a rank perception optimization (RPO) loss is proposed in the FDPS framework to enhance the discrimination and diversity of inter-class features. Experimental results on PRW and CUHK-SYSU datasets demonstrate the effectiveness of the proposed method.