Towards robust automatic traffic scene analysis in real-time
Koller, D.; Weber, J.; Huang, T.; Malik, J.; Ogasawara, G.; Rao, B.; Russell, S.
Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on
Volume 1, Issue , 9-13 Oct 1994 Page(s):126 - 131 vol.1
Digital Object Identifier 10.1109/ICPR.1994.576243
Summary: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 the authors to develop a system for detailed, reliable traffic
scene analysis. The machine vision component of the 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, the authors discuss the key tasks of the vision and
reasoning components as well as their integration into a working
prototype. Preliminary results of an implementation on special purpose
hardware using C-40 Digital Signal Processors show that near real-time
performance can be achieved without further improvements
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