Robust Multiple Car Tracking with Occlusion Reasoning
Joseph Weber , and
University of California at Berkeley, Technical Report UCB:CSD-93-780,
In this paper we address the problem of traffic surveillance in an Advanced
Transportation Management System. We propose a new approach for detecting and
tracking vehicles in road traffic scenes
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 the
case of occlusions.
The document is available online in
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Berkeley, January, 1994.
Last modified on Tuesday, November 20, 1996,