Moving Object Segmentation and Tracking Using Active Contour and Color Classification Models

This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature by using the K-means algorithm. Subsequently, the video object in the next frame is automatically tracked by using temporal differencing and block matching. The moving and stationary regions in a frame are estimated by the temporal differencing. In the moving regions, pixels are obtained their classification from the previous frame using block matching while they are directly received their classification from the previous frame in the stationary regions. Experimental results show that the proposed method provides better performance than the active contour method applied in video object tracking.