From Stixels To Objects - A Conditional Random Field Based Approach

Detection and tracking of moving traffic participants like vehicles, pedestrians or bicycles from a mobile platform using a stereo camera system plays a key role in traffic scene understanding and for future driver assistance and safety systems. To this end, this work presents a Bayesian segmentation approach based on the Dynamic Stixel World, an efficient super-pixel object representation. The existence and state estimation of an (initially) unknown number of moving objects and the detection of stationary background is formulated as a time-recursive energy minimization problem that can be solved in real-time by means of the alpha-expansion multi-class graph cut optimization scheme. In order to handle noise, this approach integrates 3D and motion features as well as spatio-temporal prior knowledge in a probabilistic conditional random field (CRF) framework. An optional fusion step with an additional radar sensor combines the advantages of both measuring instruments and yields superior overall results. The performance and robustness of the presented approach is evaluated quantitatively in various challenging traffic scenes.