Fast and Effective Motion Model for Moving Object Detection Using Aerial Images

Motion detection remains an unsolved issue for moving object detection using aerial images from moving vehicle like Unmanned Aerial Vehicle due to lack of motion model. Existing moving object detection methods do not provide motion model to detect motion pixels. In addition, previous research for moving object detection depends on either frame difference or segmentation methods. Frame difference based approaches can differentiate pixel motion but cannot extract the overall object whereas segmentation approaches can extract the overall object but cannot differentiate object motion. However, moving object performance depends on the feature type(s) employed, due to limited feature availability from aerial images. The purpose of the current research is first to select a new feature for overall detection procedures, second to propose a model for motion detection, and third to apply frame difference and segmentation methods together to achieve optimum detection performance. A new motion model, Advanced Moment based Motion Unification (AMMU) is proposed, where the moment feature is used for motion detection. Experimental results verify that the proposed AMMU model is successful at detection of moving objects.