An Autonomous Vision-Based Target Tracking System for Rotorcraft Unmanned Aerial Vehicles

In this paper, an autonomous vision-based tracking system is presented to track a maneuvering target for a rotorcraft unmanned aerial vehicle (UAV) with an onboard gimbal camera. To handle target occlusions or loss for real-time tracking, a robust and computationally efficient visual tracking scheme is considered using the Kernelized Correlation Filter (KCF) tracker and the redetection algorithm. The states of the target are estimated from the visual information. Moreover, feedback control laws of the gimbal and the UAV using the estimated states are proposed for the UAV to track the moving target autonomously. The algorithms are implemented on an onboard TK1 computer, and extensive outdoor flight experiments have been performed. Experimental results show that the proposed computationally efficient visual tracking scenario can stably track a maneuvering target and is robust to target occlusions and loss.