GPAN-PS: Global-Response Pedestrian Attention Network for End-to-End Person Search

Person search, which involves identifying target pedestrians in extensive galleries through person detection and re-identification, has experienced significant advancements across various applications. However, it remains a challenging research area due to factors such as appearance changes, lighting variations, background interference, and pedestrian occlusion. This paper proposes an end-to-end person search framework, termed the Global-Response Pedestrian Attention Network (GPAN-PS), designed to tackle these challenges. Specifically, GPAN-PS includes a novel Global Response Pedestrian Attention (GRPA) module that samples pedestrian features using three shared-weight convolutional layers with distinct dilation rates. This enables the network to adaptively select the optimal receptive field through the Squeeze-and-Excitation (SE) module and the Global Response Normalization (GRN) module, enhancing feature stability. Furthermore, we design a GsConvNeXt Head module to bolster feature expressiveness and facilitate inter-channel information interaction. Rather than employing the ConvNeXt (conv5) module as the Box Head for generating refined proposals, our approach employs the GsConvNeXt Head module. This module is also integrated into the Re-ID Head for the extraction of pedestrian features. Both the GRPA and GsConvNeXt Head modules are flexible and adaptable, allowing for seamless integration into other models. Extensive experiments conducted on two benchmark datasets, CUHK-SYSU and PRW, underscore the superior performance of our proposed method. Notably, on the challenging PRW dataset, our approach achieves a mean Average Precision (mAP) of 59.2% and a Top-1 accuracy of 92.2%.