A hybrid approach to detection and tracking of unmanned aerial vehicles

This paper proposes a novel, hybrid vision-based system to autonomously detect and track a specific moving target, in our case an evader UAV (Unmanned Aerial Vehicles), with a moving camera. The framework is based on a detection stage which exploits a Faster Region-based Convolutional Neural Network (Faster R-CNN) designed to detect the Region Of Interest (ROI) associated to the UAV’s position in the image plane. The moving target is tracked by using an Optical Flow-based tracking system and a Kalman Filter is used to give temporal consistency between consecutive measurements. The tracking system is designed to be able to achieve real-time image processing on embedded systems, for this reason a lag compensation algorithm for the delay due to the Faster R-CNN computation time is implemented. Algorithm’s performance is evaluated by computing the error between the true UAV position in the image plane and the estimated position resulting from the tracking system.