Automatic video object segmentation using depth information and an active contour model

Automatic video object segmentation based on spatial-temporal information has been a research topic for many years. Existing approaches can achieve good results in some cases, such as where there is a simple background. However, in the case of cluttered backgrounds or low quality video input, automatic video object segmentation is still a problem without a general solution. A novel approach is introduced in this work, to deal with this problem by using depth information in the algorithm. The proposed approach obtains the initial object masks based on depth map and on motion detection. The object boundaries are obtained by updating object masks using a simultaneous combination of multiple cues, including spatial location, intensity, and edge, within an active contour model. The experimental result shows that this method is effective and has good output, even with cluttered backgrounds. It is also robust when the quality of input depth and video is low.