Scale-Adaptive Vehicle Tracking Algorithm in UAV Scene

Abstract

In order to solve the problem of model drift caused by the scale change of the target vehicle when the traditional correlation filtering algorithm tracks the vehicle in the video taken by the UAV (unmanned aerial vehicle), this paper proposes an improved scale adaptive vehicle tracking algorithm. The algorithm is based on nuclear correlation filtering. By constructing a spatial tracker that distinguishes scales, this paper uses two filters, one to locate the target vehicle??s position, the other to estimate the scale of the target vehicle, in order to quickly determine target-related information and achieve scale adaptation. In addition, in order to solve the problem of poor tracking performance caused by rapid deformation of the target vehicle, color features that are less sensitive to deformation are added to increase the robustness of the filter, and the statistical color feature method is adopted, which is not restricted by template features. The improved algorithm in this paper is tested on 28 vehicle-related video sequences in OTB and UAV data sets. The average distance accuracy is 80.8%, the average success rate is 82.7%, and the FPS reaches 58.24. Experiments show that the algorithm in this paper can improve the detection and tracking effect of vehicles in the drone scene, and can effectively solve the problems caused by the scale change and rapid deformation of the target vehicle. Compared with other nuclear-related filtering algorithms, it has better tracking accuracy and real-time performance.

Keywords

• unmanned aerial vehicle (uav) scene
• kernel correlation filtering
• scale adaptation
• statistical color feature