Machine Vision Based Traffic Surveillance
An output of the machine vision based traffic surveillance system, producing tracks of
the detected cars.
Caltrans (California Department of Transportation) is funding a project at UC
to investigate the applicability of using machine vision for traffic surveillance
of california highways.
This is a joint project of the Vision Group of Prof. Jitendra
Malik for investigating the machine vision components, and the AI Group of
Prof. Stuart Russell for symbolic reasoning (e.g. detecting abberant driver behaviour
or lane changes) using the raw traffic data extracted from
the machine vision component. A major constraint is that this system is supposed
to run in (near) real-time in the near future, i.e. we can not use any sophisticated,
complex model based approaches, but simple and fast algorithms.
Our current approach is based on 2D contour tracker that attains a level of accuracy
and reliability which lies beyond currently
available systems. High accuracy and reliability are obtained by using an
explicit occlusion reasoning step. For that purpose we employ a
contour tracker based on intensity and motion boundaries. The motion of the contour
of the vehicles in the image is assumed to be well describable by an affine motion
model with a translation and a change in scale. A contour associated to a
moving region is initialized using a
motion segmentation step which is based on image differencing between an acquired
image and a continuously updated background image.
A vehicle contour is represented by a closed cubic spline
the position and motion of which is estimated along the image sequence.
In order to employ linear Kalman Filters we decompose the estimation process in two
filters: one for estimating the affine motion parameters
and one for estimating the shape of the contours of the vehicles.
Occlusion detection is performed by intersecting the depth ordered regions associated
to the objects. The intersection is then excluded in the motion and shape
This procedure also improves the shape estimation in case of adjacent objects since
occlusion detection is performed on slightly enlarged regions.
In this way we obtain robust motion estimates and trajectories for vehicles even in
case of occlusions.
Towards Realtime Visual Based Tracking in Cluttered Traffic Scens.
- D. Koller, J. Weber, J. Malik.
In Proc. of the Intelligent Vehicles Symposium, pp. 201-206, Paris, France,
October 24-26, 1994.
Towards Robust Automatic Traffic Scene Analysis in Real-Time.
- D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao,
and S. Russell.
In Proc. of the 12th Int'l Conference on Pattern Recognition, pp. 126-131,
Jerusalem, Israel, October 9-13, 1994
Automatic Symbolic Traffic Scene Analysis Using Belief Networks.
- T. Huang, D. Koller, J. Malik, G. Ogasawara, B. Rao, S. Russell,
In Proc. of the 12th National Conference on Artificial Intelligence, 1994.
Robust Multiple Car Tracking with Occlusion Reasoning.
- D. Koller, J. Weber, J. Malik.
In Proc. Third European Conference on Computer
Vision, pp. 186-196, May 2-6, 1994, LNCS 800, Springer-Verlag, 1994.
Also Technical report UCB/CSD-93-780, October 1993 and
California PATH Working Paper UCB-ITS-PWP-94-01
(ISSN 1055-1417), January 1994.
Berkeley, May 26, 1994.
Last modified on Tuesday, November 20, 1996,