Efficient representation of traffic scenes by means of dynamic stixels

Correlation based stereo vision has proven its power in commercially available driver assistance systems. Recently, real-time dense stereo vision has become available on inexpensive FPGA hardware. In order to manage the huge amount of data, a medium-level representation named “Stixel World” has been proposed for further analysis. In this representation the free space in front of the vehicle is limited by adjacent rectangular sticks of a certain width. Distance and height of each so called stixel are determined by those parts of the obstacle it represents. This Stixel World is a compact but flexible representation of the three-dimensional traffic situation. The underlying model assumption is that objects stand on the ground and have approximately vertical pose with a flat surface. So far, this representation is static since it is computed for each frame independently. Driver assistance, however, is most interested in pose and motion of moving obstacles. For this reason, we introduce tracking of stixels in this paper. Using the 6D-Vision Kalman filter framework, lateral as well as longitudinal motion is estimated for each stixel. That way, the grouping of stixels based on similar motion as well as the detection of moving obstacles turns out to be significantly simplified. The new dynamic Stixel World has proven to be well suited as a common basis for the scene understanding tasks of driver assistance and autonomous systems.