Automatic Symbolic Traffic Scene Analysis Using Belief Networks

Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.

[1]  S. Lauritzen.  Propagation of Probabilities, Means, and Variances in Mixed Graphical Association Models , 1992 .

[2]  David Chapman,et al.  Pengi: An Implementation of a Theory of Activity , 1987, AAAI.

[3]  Andrew Blake,et al.  Dynamic contours: real-time active splines , 1993 .

[4]  Robert F. Stengel,et al.  Rule-Based Guidance for Vehicle Highway Driving in the Presence of Uncertainty , 1991, 1991 American Control Conference.

[5]  Hans-Hellmut Nagel,et al.  Algorithmic characterization of vehicle trajectories from image sequences by motion verbs , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Jörg R. J. Schirra,et al.  From image sequences to natural language: a First step toward automatic perception and description of motions , 1987, Appl. Artif. Intell..

[7]  Ann Elizabeth Nicholson,et al.  Monitoring discrete environments using dynamic belief networks (robotics) , 1992 .

[8]  M. Kilger,et al.  A shadow handler in a video-based real-time traffic monitoring system , 1992, [1992] Proceedings IEEE Workshop on Applications of Computer Vision.

[9]  Kristian G. Olesen,et al.  HUGIN - A Shell for Building Bayesian Belief Universes for Expert Systems , 1989, IJCAI.

[10]  Hans-Hellmut Nagel,et al.  Berechnung von Bewegungsverben zur Beschreibung von aus Bildfolgen gewonnenen Fahrzeugtrajektorien in Straßenverkehrsszenen , 1991, Inform. Forsch. Entwickl..

[11]  B. Barsky,et al.  An Introduction to Splines for Use in Computer Graphics and Geometric Modeling , 1987 .

[12]  K. P. Karmann,et al.  Moving object recognition using an adaptive background memory , 1990 .

[13]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[14]  C. Robert Kenley,et al.  Gaussian influence diagrams , 1989 .

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

[16]  Andrew Blake,et al.  Affine-invariant contour tracking with automatic control of spatiotemporal scale , 1993, 1993 (4th) International Conference on Computer Vision.

[17]  Stuart Russell,et al.  Symbolic Traffic Scene Analysis Using Dynamic Belief Networks , 1993 .

[18]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.