ML Anthology

Model-Based Validation Approaches and Matching Techniques for Automotive Vision Based Pedestrian Detection

Alberto Broggi, Alessandra Fascioli, Paolo Grisleri, Thorsten Graf, Marc-Michael Meinecke
CVPRW 2005 pp. 1
doi:10.1109/CVPR.2005.495 /cvprw/2005/broggi2005cvprw-modelbased/

Abstract

Pedestrian detection is a challenging vision task, especially applied to the automotive field where the background changes as the vehicle moves. This paper presents an extensive study upon human body models and the techniques suitable for being used in a pedestrian detection system. Several different approaches for building model sets, such as synthetic, real, and dynamic sets are presented and discussed. Comparative results are reported with reference to a case study of a real system. Preliminary results of current research status are shown together with further developments.

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Cite

Text

Broggi et al. "Model-Based Validation Approaches and Matching Techniques for Automotive Vision Based Pedestrian Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2005. doi:10.1109/CVPR.2005.495

Markdown

[Broggi et al. "Model-Based Validation Approaches and Matching Techniques for Automotive Vision Based Pedestrian Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2005.](https://mlanthology.org/cvprw/2005/broggi2005cvprw-modelbased/) doi:10.1109/CVPR.2005.495

BibTeX

@inproceedings{broggi2005cvprw-modelbased,
  title     = {{Model-Based Validation Approaches and Matching Techniques for Automotive Vision Based Pedestrian Detection}},
  author    = {Broggi, Alberto and Fascioli, Alessandra and Grisleri, Paolo and Graf, Thorsten and Meinecke, Marc-Michael},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2005},
  pages     = {1},
  doi       = {10.1109/CVPR.2005.495},
  url       = {https://mlanthology.org/cvprw/2005/broggi2005cvprw-modelbased/}
}