Real-time Pedestrian Tracking Based on YOLOv3 and Prototype Clustering

Abstract

Although the accuracy of existing neural network models is high, in pedestrian tracking tasks, due to the uncertainty of targets, when tracking new targets, it is necessary to fine-tune the model, which further requires large computing and storage resource overhead. Therefore, its application on some lightweight platforms, such as robots and UAVs, is limited. Pedestrian tracking by robots and UAVs still faces great challenges in occlusion, multi-target, target loss, etc. This paper mainly solves the problem of real-time pedestrian tracking by object detection model of robot lightweight, which is mainly based on YOLO network to detect pedestrians, and then proposes a novel lightweight model and prototype clustering algorithm. Numerous experiments on the ETH dataset validate the superiority and effectiveness of our approach.