Superpixel-based obstacle segmentation from dense stereo urban traffic scenarios using intensity, depth and optical flow information

Obstacle detection is a necessary task in every driving assistance system. An accurate obstacle segmentation is very important for further processing tasks that are using the obstacle ROI as input, like obstacle classification. This paper presents a real time approach for obstacle segmentation from traffic scenarios, based on superpixels clustering. A pair of gray levels stereo-cameras is used for scene image acquisition. The stereo-reconstruction uses a sub-pixel level optimized semi-global matching (SORT-SGM) resulting in a very accurate 3D points map. Optical flow is computed using a Lukas-Kanade pyramidal approach. A novel paradigm integrating intensity, depth and optical flow information on superpixels is used for obstacle segmentation. SLIC superpixels are computed first based on intensity information. Multiple features are computed for each superpixel and used for clustering superpixels in obstacles. Depth cues are used for clustering the superpixels in obstacles and then optical flow information refines the obstacles clusters based on their motion. A qualitative and quantitative evaluation of the proposed approach and a comparison with other obstacle detection technique are finally presented.