Vision for Longitudinal Vehicle

An important component of the drive towards intelligent vehicles is the ability to maintain a xed distance from a lead vehicle using feedback provided by range sensors. We are investigating the possibility of using stereo vision to provide the range information, in conjunction with a scanning laser radar sensor. The vision algorithms build on xation and reconstruction algorithms designed for active vision systems, and combine stereo and motion cues. We shall present preliminary results comparing the quality of range measurements provided by a vision system with the laser radar system, using data obtained oo-line. Later we will implement the tracker in real time on a network of C40 DSPs, and combine the laser and vision sensing in a cooperative manner. Note to reviewers: a longer version of this paper has been submitted to the IEEE Intelligent Transportation Systems conference in Boston. At BMVC we shall present new results from the real-time system currently under development. Abstract An important component of the drive towards intelligent vehicles is the ability to maintain a xed distance from a lead vehicle using feedback provided by range sensors. We are investigating the possibility of using stereo vision to provide the range information, in conjunction with a scanning laser radar sensor. The vision algorithms build on xation and reconstruction algorithms designed for active vision systems, and combine stereo and motion cues. We shall present preliminary results comparing the quality of range measurements provided by a vision system with the laser radar system, using data obtained oo-line. Later we will implement the tracker in real time on a network of C40 DSPs, and combine the laser and vision sensing in a cooperative manner. Note to reviewers: a longer version of this paper has been submitted to the IEEE Intelligent Transportation Systems conference in Boston. At BMVC we shall present new results from the real-time system currently under development.

[1]  Jitendra Malik,et al.  Vision for longitudinal vehicle control , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[2]  Emanuele Trucco,et al.  Geometric Invariance in Computer Vision , 1995 .

[3]  David W. Murray,et al.  A unifying framework for structure and motion recovery from image sequences , 1995, Proceedings of IEEE International Conference on Computer Vision.

[4]  Stephen M. Smith,et al.  ASSET-2: real-time motion segmentation and shape tracking , 1995, Proceedings of IEEE International Conference on Computer Vision.

[5]  Andrew Zisserman,et al.  Robust detection of degenerate configurations for the fundamental matrix , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  H Kikuchi,et al.  Development of Laser Radar for Radar Brake System , 1995 .

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

[8]  Ian D. Reid,et al.  Recursive Affine Structure and Motion from Image Sequences , 1994, ECCV.

[9]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[10]  Olivier D. Faugeras,et al.  A Stability Analysis of the Fundamental Matrix , 1994, ECCV.

[11]  Paul A. Beardsley,et al.  Robust Vision , 1994, BMVC.

[12]  Ian D. Reid,et al.  Saccade and pursuit on an active head/eye platform , 1994, Image Vis. Comput..

[13]  Patrick Bouthemy,et al.  Motion detection robust to perturbations: A statistical regularization and temporal integration framework , 1993, 1993 (4th) International Conference on Computer Vision.

[14]  Ian D. Reid,et al.  Tracking foveated corner clusters using affine structure , 1993, 1993 (4th) International Conference on Computer Vision.

[15]  Chris Harris,et al.  Tracking with rigid models , 1993 .

[16]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Olivier Faugeras,et al.  3D Dynamic Scene Analysis , 1992 .

[18]  Peter J. Burt,et al.  Object tracking with a moving camera , 1989, [1989] Proceedings. Workshop on Visual Motion.

[19]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.