E3ID: An efficient end to end person search model

Abstract

Person Search has recently emerged as a challenging task that aims to jointly solve Pedestrian Detection and Person Re-identification (Re-ID). However, the existing approaches still stay at the image processing stage. In this paper, we proposed E3ID, an efficient end-to-end person search model for video, to better solve the person search problem in real densely populated areas. To speed up the model, a low-quality image filter is proposed to adaptively adjust the sampling frequency of Yolov5 according to the moving speed of pedestrians in the video with minimal computational effort. And the cropped pedestrian images are stored in the gallery library for the Re-ID step. Then, we exploit color feature enhancer to mine color features in specific regions that contribute significantly to the Re-ID process. Compared to other state-of-the-art methods, our proposed E3ID can reach 77.94 % on the evaluation index rank 1, which creates a feasibility of using this model in other complex real-world scenarios.