Stereo-Based Object Segmentation Combining Spatio-Temporal Information

In the case of cluttered backgrounds or low quality video input, automatic video object segmentation based on spatial-temporal information is still a problem without a general solution. A new approach is introduced in this work to deal with this problem by using depth information. The proposed approach obtains the initial object masks based on depth density image and motion segmentation. The objects boundaries are obtained by updating object masks using a simultaneous combination of multiple cues, including spatial location, colour, depth and motion, within a maximum likelihood method. The experimental result shows that this method is effective and has good output in cluttered backgrounds.