High confidence detection for moving target in aerial video

Abstract

The moving target detection and tracking in aerial video is a challenge task because of its moving background, smaller target sizes, lower resolution and limited onboard computing resources. In this study, a high confidence detection method based on background compensation and three-frame-difference method is designed, which can detect moving objects in a dynamic background accurately. First, the authors use local feature extraction and matching for image registration and demonstrate that speed-up robust feature key points are suitable for the stabilisation task. Then, they estimate the global camera motion parameters using affine transformation which are obtained by the random sample consensus algorithm. Finally, they detect moving object by three-frame-difference method. As the detection results of the frame-difference method generally exists ‘empty’ and noise, in order to select the two higher-quality differential images to perform the logic AND operation, they add image quality assessment to the three-frame-difference method to obtain more accurate moving objects. Moreover, the edge detection algorithm and morphological processing are integrated together to further boost the overall detecting performance. The extensive empirical evaluations on aerial videos demonstrate that the proposed detector is very promising for the various challenging scenarios.