Object-Level Priors for Stixel Generation

This paper presents a stereo vision-based scene model for traffic scenarios. Our approach effectively couples bottom-up image segmentation with object-level knowledge in a sound probabilistic fashion. The relevant scene structure, i.e. obstacles and freespace, is encoded using individual Stixels as building blocks that are computed bottom-up from dense disparity images. We present a principled way to additionally integrate top-down prior information about object location and shape that arises from independent system modules, ranging from geometric cues up to highly confident object detections. This results in an efficient exploration of orthogonal image-based cues, such as disparity and graylevel intensity data, combined in a consistent scene representation. The overall segmentation problem is modeled as a Markov Random Field and solved efficiently through Dynamic Programming. We demonstrate superior segmentation accuracy compared to state-ofthe-art superpixel algorithms regarding obstacles and freespace in the scene, evaluated on a large dataset captured in real-world traffic.