Small target tracking using KCF and adaptive thresholding

Abstract

Recently, visual object tracking has become a vital role in many different applications. It spans various applications like robotics, video surveillance, and intelligent vehicle navigation. In the Counter-UAV (Unmanned Aerial Vehicle) system, in order to shoot down illegal UAVs flying in the sky, it is very important task to accurately track UAV by an image sensor. However, accurate target tracking is a difficult problem because the distance to the target is far, there are severe shape changes, and the target is often obscured. The remarkable method in object tracking is correlation filter based tracker like KCF (Kernelized Correlation Filter). Although the tracker is very competitive in terms of computation time and performance among the state-of-the-art trackers, it has problems related to target drift in tracking process. It comes from that target objects undergo significant appearance variation due to deformation, partial occlusion, camera shaking, and illumination changes. To mitigate the drift problem of the target in CUAV application, we propose a tracking method which uses KCF and adaptive thresholding. When the correlation response is less than a certain value during KCF process, the result of adaptive thresholding adjusts the tracking point. In the experiment, the proposed method was verified by using image sequence obtained by a monochrome camera on the CUAV system.