A block based moving object detection utilizing the distribution of noise

Moving object segmentation in complex scene is the basis for video surveillance, event detection, tracking and development of vision agent in industrial robotics. However, due to presence of camera noise and illumination change, simple background subtraction based techniques are not able to detect moving objects properly. In this paper, we present a novel block based moving object detection method which dynamically quests for both local and global properties of difference image to achieve robustness. Noise model of the difference image is determined analyzing the histogram of difference image and block wise local properties are computed. These local properties are compared with the noise model to extract moving blocks. To remove the stair like artifacts of the segmented result, and to obtain smoothed and accurate boundary, a refinement procedure is employed on the boundary regions of detected moving objects. Experimental results show that the proposed method is robust and achieves better performance in dynamic environment.