Plan-View Trajectory Estimation with Dense Stereo Background Models

In a known environment, objects may be tracked in multiple views using a set of background 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 "latesegmentation" 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.

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Trevor Darrell, David Demirdjian, Neal Checka, Pedro F. Felzenszwalb
• Keywords: Computer Vision, Dense Stereo Models, Foreground Points, ICCV 2001, Object Regions, Object Tracking, Special Spatial Zones