Moving vehicle detection by optimal segmentation of the Dynamic Stixel World

The reliable detection of moving objects from a moving observer is one of the most challenging and important tasks for driver assistance and safety systems. Modern sensors such as Lidar, Imaging Radar or Stereo Vision deliver range data plus longitudinal motion (Radar) or even full 3D-motion (space-time vision). Based on this data, moving objects have to be separated from the static background to be able to determine their pose and motion state. Usually, heuristics are applied to cluster the data. In order to find the most probable segmentation, we formulate the task as a hypotheses testing problem that allows taking into account various constraints and assumptions simultaneously. We show that the optimal segmentation can be efficiently found by means of dynamic programming, for an arbitrary number of objects in the scene. In this paper we concentrate on the segmentation of space-time data obtained from stereo image sequences. The vision-based depth and motion information is transferred into so called Stixels, a very compact representation of 3D scenes that can also be applied to Lidar or Radar data. It turns out that our optimal segmentation is more robust w.r.t. noisy and erroneous data.