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Plan-view Trajectory Estimation with Dense Stereo Background Models

Author(s)
Darrell, T.; Demirdjian, D.; Checka, N.; Felzenswalb, P.
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Abstract
In a known environment, objects may be tracked in multiple views using a set of back-ground models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a 'late-segmentation' strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones.
Date issued
2001-02-01
URI
http://hdl.handle.net/1721.1/6075
Other identifiers
AIM-2001-001
Series/Report no.
AIM-2001-001

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