ML Anthology

Stereo-Based Object Detection, Classi?cation, and Quantitative Evaluation with Automotive Applications

Peng Chang, David J. Hirvonen, Theodore Camus, Ben Southall
CVPR 2005 pp. 62
doi:10.1109/CVPR.2005.535 /cvpr/2005/chang2005cvpr-stereo/

Abstract

A real-time stereo-based pre-crash object detection and classification system is presented. The system employs a model based stereo object detection algorithm to find candidate objects from the scene, followed by a Bayesian classification framework to assign each candidate to its proper class. Our current system detects and classifies several types of objects commonly seen for automotive applications, namely vehicles, pedestrians/bikes, and poles. We describe both the detection and classification algorithms in detail along with real-time implementation issues. A quantitative analysis of performance on a static data set is also presented.

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Cite

Text

Chang et al. "Stereo-Based Object Detection, Classi?cation, and Quantitative Evaluation with Automotive Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.535

Markdown

[Chang et al. "Stereo-Based Object Detection, Classi?cation, and Quantitative Evaluation with Automotive Applications." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/chang2005cvpr-stereo/) doi:10.1109/CVPR.2005.535

BibTeX

@inproceedings{chang2005cvpr-stereo,
  title     = {{Stereo-Based Object Detection, Classi?cation, and Quantitative Evaluation with Automotive Applications}},
  author    = {Chang, Peng and Hirvonen, David J. and Camus, Theodore and Southall, Ben},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {62},
  doi       = {10.1109/CVPR.2005.535},
  url       = {https://mlanthology.org/cvpr/2005/chang2005cvpr-stereo/}
}