Multi-target detection and tracking from a single camera in unmanned aerial vehicles (UAVs)

Despite the recent flight control regulations, Unmanned Aerial Vehicles (UAVs) are still gaining popularity in civilian and military applications, as much as for personal use. Such emerging interest is pushing the development of effective collision avoidance systems. Such systems play a critical role UAVs operations especially in a crowded airspace setting. Because of cost and weight limitations associated with UAVs payload, camera based technologies are the de-facto choice for collision avoidance navigation systems. This requires multitarget detection and tracking algorithms from a video, which can be run on board efficiently. While there has been a great deal of research on object detection and tracking from a stationary camera, few have attempted to detect and track small UAVs from a moving camera. In this paper, we present a new approach to detect and track UAVs from a single camera mounted on a different UAV. Initially, we estimate background motions via a perspective transformation model and then identify distinctive points in the background subtracted image. We find spatio-temporal traits of each moving object through optical flow matching and then classify those candidate targets based on their motion patterns compared with the background. The performance is boosted through Kalman filter tracking. This results in temporal consistency among the candidate detections. The algorithm was validated on video datasets taken from a UAV. Results show that our algorithm can effectively detect and track small UAVs with limited computing resources.