Pedestrian Multi-Objective Tracking Based on Work-Yolo

Abstract

Aiming at the problem of low pedestrian tracking accuracy caused by illumination and occlusion in intelligent surveillance videos, a pedestrian tracking algorithm based on Work-Yolo detection combined with DeepSORT is proposed. To improve the accuracy of the detector, the attention module CBAM is used to fuse with the Backbone and Neck parts of Yolov5s network for enhancing pedestrian features. The BiAdd structure is used to fuse features of different scales about BiFPN, Dilated ConV is proposed to reduce the number of model parameters and extract better shallow features. Work-Yolo head decoupled head separates prediction classification and regression tasks, solving the problem of missed detection when pedestrians are obscured. The Lite-shufflenet lightweight structure is proposed to extract appearance features, and retrain the pedestrian re-identification dataset to reduce the identity switching caused by pedestrian occlusion. Pedestrian detection experiments are conducted on 3247 intersection pedestrian datasets, and the final detection accuracy rate was 94.2% and the recall rate was 90.6%. The video taken at the indoor entrance and exit of the scene, four videos are randomly selected for multi-target tracking experiments, the MOTA is improved by 10%, and the detection speed reaches 15fps, which meets the requirements of industrial applications.