Real-time and accurate segmentation of moving objects in dynamic scene
Fast and accurate segmentation of moving objects in video sequences is a basic task in many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and classification and activity analysis. This paper presents effective methods for solving this problem. Firstly, a fast and efficient algorithm is presented for background update to handle various sources of scene changes, including ghosts, left objects, camera shaking, and abrupt illumination changes. This is done by analyzing properties of object motion in image pixels and temporal frames, and combining both levels of constraints. Moreover, the algorithm does not need training sequence. Secondly, a real-time and accurate moving object segmentation algorithm is presented for moving object localization. Here, a novel filtering method is presented based on multiple scale and fast connected blob extraction. An intelligent video surveillance system is developed to test the performance of the algorithms. Experiments are performed using long video sequences under different conditions indoor and outdoor. The results show that the proposed algorithm is effective and efficient in real-time and accurate background update and moving object segmentation.