Fast detection of moving objects in complex scenarios

More than one third of all traffic accidents with injuries occur in urban areas, especially at intersections. A suitable driver assistance system for such complex situations requires the understanding of the scene, in particular a reliable detection of other moving traffic participants. This contribution shows how a robust and fast detection of relevant moving objects is obtained by a smart combination of stereo vision and motion analysis. This approach, called 6D Vision, estimates location and motion of pixels simultaneously which enables the detection of moving objects on a pixel level. Using a Kalman filter attached to each tracked pixel, the algorithm propagates the current interpretation to the next image. In addition, a Kalman filter based ego-motion compensation is described that takes advantage of the 6D information. This precise information enables us to discriminate between static and moving objects exactly and to obtain a better prediction. This speeds up tracking and a real-time implementation is achieved. Examples of critical situations in urban areas exhibit the potential of the 6D Vision concept which can also be extended to robotics applications.

[1]  Hans P. Moravec,et al.  Robot Evidence Grids. , 1996 .

[2]  Manolis I. A. Lourakis,et al.  Qualitative detection of 3D motion discontinuities , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[3]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Uwe Franke,et al.  6D-Vision: Fusion of Stereo and Motion for Robust Environment Perception , 2005, DAGM-Symposium.

[5]  Stefan Heinrich.  Real Time Fusion of Motion and Stereo Using Flow/Depth Constraint for Fast Obstacle Detection , 2002, DAGM-Symposium.

[6]  Christoph Stiller,et al.  Fusing optical flow and stereo disparity for object tracking , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[7]  J. L. Roux.  An Introduction to the Kalman Filter , 2003 .

[8]  Allen M. Waxman,et al.  Binocular Image Flows: Steps Toward Stereo-Motion Fusion , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  C. Rabe,et al.  Kalman filter based depth from motion with fast convergence , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[10]  Uwe Franke,et al.  Real-time stereo vision for urban traffic scene understanding , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).