Spider-based Stixel object segmentation

Stereo vision has established in the field of driver assistance and vehicular safety systems. Next steps along the road towards accident free driving aim to assist the driver in increasingly complex situations such as inner-city traffic. In order to achieve these goals, it is desirable to incorporate higher-order object knowledge in the stereo vision-based understanding of traffic scenes. In particular, object shape and dimension information can help to achieve correct interpretations. However, typically this kind of higher-order information results in a difficult energy minimization problem since large areas of the input image have to be constrained. In this contribution, an efficient global optimization approach based on dynamic programming is proposed that is able to take into account such higher-order object knowledge. The approach is built upon a simple tree representation of the Dynamic Stixel World, an efficient super-pixel object representation. Experiments show that object segmentation can be improved significantly by means of the higher-order object information.