Plan-View Trajectory Estimation with Dense Stereo Background Models

Trevor Darrell, David Demirdjian, Neal Checka, Pedro F. Felzenszwalb

ICCV 2001 pp. 628-635

doi:10.1109/ICCV.2001.937685 /iccv/2001/darrell2001iccv-plan/

Abstract

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 "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.

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Text

Darrell et al. "Plan-View Trajectory Estimation with Dense Stereo Background Models." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.937685

Markdown

[Darrell et al. "Plan-View Trajectory Estimation with Dense Stereo Background Models." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/darrell2001iccv-plan/) doi:10.1109/ICCV.2001.937685

BibTeX

@inproceedings{darrell2001iccv-plan,
  title     = {{Plan-View Trajectory Estimation with Dense Stereo Background Models}},
  author    = {Darrell, Trevor and Demirdjian, David and Checka, Neal and Felzenszwalb, Pedro F.},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2001},
  pages     = {628-635},
  doi       = {10.1109/ICCV.2001.937685},
  url       = {https://mlanthology.org/iccv/2001/darrell2001iccv-plan/}
}