Distance/motion-based segmentation under heavy background noise

Typical segmentation algorithms are challenged by background noise and the variation of object sizes and object positions in video frames. In this paper, we propose a new object segmentation method based on both motion and distance information to increase segmentation reliability and to suppress background noise. Two new concepts are described in this paper. First proposed is a new distance-based background detection algorithm to remove the impact of noisy background without using reference frames. The second proposed is a new depth/motion-based segmentation that can accurately capture objects of different sizes. The algorithm introduced successfully increases the accuracy and reliability of object segmentation and motion detection.