ML Anthology

Bridging the Gap Between Detection and Tracking for 3D Monocular Video-Based Motion Capture

Andrea Fossati, Miodrag Dimitrijevic, Vincent Lepetit, Pascal Fua
CVPR 2007
doi:10.1109/CVPR.2007.383297 /cvpr/2007/fossati2007cvpr-bridging/

Abstract

We combine detection and tracking techniques to achieve robust 3-D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3-D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the case of people walking against cluttered backgrounds and filmed using a moving camera, which precludes the use of simple background subtraction techniques. In this case, the easy-to-detect posture is the one that occurs at the end of each step when people have their legs furthest apart.

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Text

Fossati et al. "Bridging the Gap Between Detection and Tracking for 3D Monocular Video-Based Motion Capture." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007. doi:10.1109/CVPR.2007.383297

Markdown

[Fossati et al. "Bridging the Gap Between Detection and Tracking for 3D Monocular Video-Based Motion Capture." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2007.](https://mlanthology.org/cvpr/2007/fossati2007cvpr-bridging/) doi:10.1109/CVPR.2007.383297

BibTeX

@inproceedings{fossati2007cvpr-bridging,
  title     = {{Bridging the Gap Between Detection and Tracking for 3D Monocular Video-Based Motion Capture}},
  author    = {Fossati, Andrea and Dimitrijevic, Miodrag and Lepetit, Vincent and Fua, Pascal},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2007},
  doi       = {10.1109/CVPR.2007.383297},
  url       = {https://mlanthology.org/cvpr/2007/fossati2007cvpr-bridging/}
}