Motion Segmentation and Depth Ordering Using an Occlusion Detector

We present a novel method for motion segmentation and depth ordering from a video sequence in general motion. We first compute motion segmentation based on differential properties of the spatio-temporal domain and scale-space integration. Given a motion boundary, we describe two algorithms to determine depth ordering from two- and three-frame sequences. A remarkable characteristic of our method is its ability compute depth ordering from only two frames. The segmentation and depth ordering algorithms are shown to give good results on six real sequences taken in general motion. We use synthetic data to show robustness to high levels of noise and illumination changes; we also include cases where no intensity edge exists at the location of the motion boundary or when no parametric motion model can describe the data. Finally, we describe psychophysical experiments showing that people, like our algorithm, can compute depth ordering from only two frames even when the boundary between the layers is not visible in a single frame.