Online Drone-Based Moving Target Detection System in Dense-Obstructer Environment

Detection of moving targets is a basic but challenging function of drone-based surveillance systems (DBSSs), which could give rise to various potential applications in smart city and intelligent transportation. However, how to detect moving targets in the dynamic high-altitude environment with restricted computational resources is one of the most critical ones. Furthermore, during the detection, the moving target could travel in dynamic speed and be blocked by dense-obstructer, such as woods and buildings. In this paper, we develop an online drone-based moving target detection (ODMTD)system, which performs moving target detection in dense-obstructer areas. Specifically, first, our proposed system simultaneously performs adaptive path planning and autonomous drone flight via the combination of historical path cost and energy loss computation by using drone attitudes. Second, to detect moving targets in the dynamic background, we develop an algorithm of combing the speed up robust features and approximate nearest neighbors, shortly SURF-ANN, for estimating and compensating the global motion of the background. Finally, in order to calibrate distorted images taken by the camera obliquely, we utilize perspective transformation to remap images into another plane, and then detect moving targets by subtracting registered images (SRI). Furthermore, real-time outdoor high-altitude experiments, by comprising with the state-of-art methods, demonstrate the effectiveness of our ODMTD system.