Spatial Invariant Person Search Network
A cascaded framework is proposed to jointly integrate the associated pedestrian detection and person re-identification in this work. The first part of the framework is a Pre-extracting Net which acts as a feature extractor to produce low-level feature maps. Then a PST (Pedestrian Space Transformer), including a Pedestrian Proposal Net to generate person candidate bounding boxes, is introduced as the second part with affine transformation and down-sampling models to help avoid the spatial variance challenges related to resolutions, viewpoints and occlusions of person re-identification. After further extracting by a convolutional net and a fully connected layer, the resulting features can be used to produce outputs for both detection and re-identification. Meanwhile, we design a directionally constrained loss function to supervise the training process. Experiments on the CUHK-SYSU dataset and the PRW dataset show that our method remarkably enhances the performance of person search.
Person re-identification Person search Spatial transformation
Liangqi Li Hua Yang Lin Chen
Shanghai Jiao Tong University,Dongchuan Road 800,Shanghai,China
国际会议
广州
英文
122-133
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）