Robust Multiple Car Tracking with Occlusion Reasoning
Joseph Weber, and
In Proc. Third European Conference on Computer Vision, Stockholm, Sweden, May 2-6,
1994, pp. 189-196, LNCS 800, Springer-Verlag, 1994.
We address the problem of occlusion in tracking multiple 3D objects in a known
environment. For that purpose we employ a
contour tracker based on intensity and motion boundaries. The motion of a contour
enclosing the image of a vehicle is assumed to be well describable by an affine motion
model with a translation and a change in scale.
Contours are represented by closed cubic splines
the position and motion of which are 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 part is then excluded in the motion and shape
Occlusion reasoning also improves the shape estimation in case of adjacent objects
shape estimates can be corrupted by image data of other objects.
In this way we obtain robust motion estimates and trajectories for vehicles even in
case of occlusions, as we show in some experiments with real world traffic scenes.
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Last modified on Tuesday, November 20, 1996,