A Lightweight Pedestrian Tracking Target Detection Algorithm

In recent years, pedestrian detection and tracking has been widely used in the fields of multimedia, computer vision and pattern recognition. Most of the traditional pedestrian tracking algorithms directly use the pixel level features in each frame of the image sequence in the video to calculate, ignoring the deep semantic feature information contained in each image, resulting in inaccurate or wrong positioning. Moreover, the traditional algorithm has a large number of parameters, high complexity and poor real-time performance. This paper proposes a new lightweight object detection algorithm Res-YOLO-tiny, which aims to accelerate the tracking efficiency of blurred image removal algorithms. It greatly improves the detection speed, and then combines with the PID (proportion integration differentiation) algorithm to increase the tracking effect and positioning accuracy of the whole tracking system through a new ROI (region of interest) combination method. Finally, the experimental and practical results show that the proposed algorithm has high positioning accuracy, sensitive tracking response, strong recognition robustness and flexibility.