A Vision-Based Target Detection, Tracking, and Positioning Algorithm for Unmanned Aerial Vehicle

Abstract

Unmanned aerial vehicles (UAV) play a pivotal role in the field of security owing to their flexibility, efficiency, and low cost. The realization of vehicle target detection, tracking, and positioning from the perspective of a UAV can effectively improve the efficiency of urban intelligent traffic monitoring. In this work, by fusing the target detection network, YOLO v4, with the detection-based multitarget tracking algorithm, DeepSORT, a method based on deep learning for automatic vehicle detection and tracking in urban environments, has been designed. With the aim of addressing the problem of UAV positioning a vehicle target, the state equation and measurement equation of the system have been constructed, and a particle filter based on interactive multimodel has been employed for realizing the state estimation of the maneuvering target in the nonlinear system. Results of the simulation show that the algorithm proposed in this work can detect and track vehicles automatically in urban environments. In addition, the particle filter algorithm based on an interactive multimodel significantly improves the performance of the UAV in terms of positioning the maneuvering targets, and this has good engineering application value.