Research on Multiple Targets Pedestrian Reidentification with Night Scene Image Enhancement

Abstract

Pedestrian reidentification is a popular research topic in the field of computer vision in recent years, and is a technique that uses computer vision techniques to determine whether a specific pedestrian is present in an image or video. After research and experiment, we found that YOLOv3-based pedestrian reidentification in practice has the problem of low accuracy rate of recognizing pedestrian pictures taken at night and cannot recognize multiple pedestrians at one time. In this paper, we improve the above problems by introducing a picture enhancement module to improve the brightness and defogging of night pictures before recognition, and improve the practice of averaging the distance values of multiple results for the same pedestrian to enable multiple targets pedestrian recognition. The experimental results demonstrate that the average accuracy rate of recognizing pedestrian pictures taken at night has improved from 6.85% to 80%, while the average accuracy rate of multiple targets pedestrian recognition has reached 85.9%, which is competent for multiple targets pedestrian recognition tasks at night.