UAV Autonomous Track Optimization Based On Multi-target Tracking

Unmanned aerial vehicles(UAV)have already applied to various fields,with its reliable maneuverability and environmental adaptability.In order to improve the intelligence of the UAV in the multi-target tracking tasks,a dynamic multi-target tracking scheme is designed.In this scheme,track multiple moving targets dynamically by the image sequence captured by the camera on the UAV,then calculate the real position of the inversion moving targets and design the UAV path for the multiple moving targets,so as to realize the multi-moving targets tracking task.The specific research contents are as follows:(1)A practical algorithm for multi-scale moving target tracking is designed.By analyzing and comparing the advantages and disadvantages of the kernel correlation filter algorithm and the mean shifting algorithm,an improved method by combining the two algorithms is designed.In the improved method,firstly,the moving target position is discriminated by the kernel correlation filter algorithm,then the target scale is estimated by the mean shift algorithm,so as to ensure the tracking efficiency and accuracy.(2)The moving target measurement method based on Kalman prediction is improved.To avoid the problem that the UAV flight direction lags behind the moving target position,the Kalman algorithm to predict the position of the moving target,and UAV flight path is pre-generated.And it can achieve the dynamic combination of target tracking data with the UAV path planning system.(3)An UAV path planning scheme for multi-moving target tracking is proposed.Aiming at the practical problems for tracking and monitoring of multi-sports target drones,by using genetic ant colony algorithm to design the UAV path,the UAV path planning scheme for multi-moving target tracking is verified.The experimental results show that the scheme can adapt to the changes of moving targets better.The trajectory accuracy and estimation efficiency are improved.