Automatic Symbolic Traffic Scene Analysis Using Belief Networks
T. Huang,
D. Koller,
J. Malik,
G. Ogasawara,
B. Rao,
S. Russell,
J. Weber
In Proc. of the 12th National Conference on Artificial Intelligence,
pp. 966-972, Seattle, WA, July 31-Aug. 4, 1994
Abstract
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.
The document is available online in
application/postscript (396297 Bytes)
Berkeley, May 26, 1994.
Last modified on Tuesday, November 20, 1996,
Dieter Koller
(koller@vision.caltech.edu)