Real-time stereo and flow-based video segmentation with superpixels

The use of depth is becoming increasingly popular in real-time computer vision applications. However, when using real-time stereo for depth, the quality of the disparity image is usually insufficient for reliable segmentation. The aim of this paper is to obtain a more accurate and at the same time faster segmentation by incorporating color, depth and optical flow. A novel real-time superpixel segmentation algorithm is presented which uses real-time stereo and realtime optical flow. The presented system provides superpixels which represent suggested object boundaries based on color, depth and motion. Each outputted superpixel has a 3D location and a motion vector, and thus allows for straightforward segmentation of objects by 3D position and by motion direction. In particular, it enables reliable segmentation of persons, and of moving hands or arms. We show that our method is competitive with the state of the art while approaching real-time performance.