Improvement of Vision-based Drone Detection and Tracking by Removing Cluttered Background, Shadow and Water Reflection with Super Resolution

Abstract

Detection and tracking of small and fast moving aerial targets especially drones has got attention nowadays. This paper focuses on the vision-based technique using images taken from observing cameras. In real life situations, the target drone objects maybe are hidden in the cluttered background such as trees shadow or foliage, rows of buildings, and other kind of scenery that will hinder the clear indication of the drones. The object is further confused by the presence of its shadow and reflection from the water or glass wall reflection. For vision-based objection, the clarity and the ambiguity of the target images in the video stream are the key for effective and successful target detection and tracking. Here, we present the improvement by mitigating the effect of cluttered background, shadow and water reflection to the target images. We applied the schemes to make the drone more visible and more clear. We also implemented super resolution to increase the image resolution for more precise detection and tracking. As a result, the target drone could be detected and tracked throughout the sample clips. The comparative tracking results using DCF are presented. Likewise, we applied the water removal scheme to eliminate the reflection to avoid confusion to the tracker. With this, just the correct drone targets were detected and tracked. Overall, the drones could be detected and tracked all the way, as long as they appeared in the camera scene.