Real-time obstacle detection using dense stereo vision and dense optical flow

Accurate detection of moving obstacles from a moving vehicle is at the core of safe autonomous driving research. Stereo vision based sensors have been extensively used for this task as they are passive and provide a large amount 3D and 2D data. However, since no motion information is revealed, in intersections or crowded urban areas, static and dynamic objects immediately next to each other, or closely positioned obstacles moving in different directions are often merged into a single obstacle leading to dangerous misinterpretations. In this paper we address these problems through a powerful fusion between dense stereo vision and dense optical flow in a depth-adaptive occupancy grid framework. The proposed fusion model is presented and then applied for obstacle detection in an intersection assistance system.