PSM-PS: Part-Based Signal Modulation for Person Search

Abstract

Person search (PS) is a computer vision problem that joins the two tasks of person detection and person re-identification (ReID). Previous works handle PS problem with either two-step or one-step approaches and have attained much attention due to complex challenges in the scene such as appearance variations, background clutter, and deformation. These approaches achieve significant performance but are still prone to performance degradation under complex scenes which may jeopardize the accuracy of person search methods. In this paper, we propose a novel Part-based Signal Modulation module for Person Search (PSM-PS) within a faster R-CNN-based person search framework. The proposed PSM module transforms the person parts, represented as part tokens, in a wave-like manner, where amplitude indicates the real part and phase shows the imaginary part in a complex domain. The proposed PSM module modulates the pedestrian part tokens such that it enhances the feature representation where the close parts of the person have a close phase compared to others. The experiments are performed over the two prominent person search datasets: CUHK-SYSU [23] and PRW [26]. The extensive experimental study demonstrates the effectiveness of our method and shows the state-of-the-art performance compared to other methods.