Dynamic Small Target Detection and Tracking Based on Hierarchical Network and Adaptive Input Image Stream

Abstract

Small target detection and dynamic target continuous tracking are difficulties in the field of target detection and tracking respectively. The experimental results show that the smaller the target size is, the lower the detection accuracy is, the more dynamic the target is, and the lower the tracking accuracy is. This paper presents a high dynamic small target detection and tracking system which is composed of a yolov4 network, KCF tracker, and Kalman filter prediction algorithm. In this system, the detection network is divided into global and local levels, in which the global network is used for initial detection and re-detection after tracking failure, while the local detection network and tracker are used for real-time tracking of small targets, a more robust system is constructed. Compared with the original target detection network, this method weakens the influence of external factors such as the change of target shape and the fast-moving speed on the detection accuracy. The recognition rate of the small target is more than 96%, and the detection accuracy achieves 78%.