ML Anthology

Real-Time Vehicle Detection for Highway Driving

Ben Southall, Mayank Bansal, Jayan Eledath
CVPR 2009 pp. 541-548
doi:10.1109/CVPR.2009.5206597 /cvpr/2009/southall2009cvpr-real/

Abstract

We present a new multi-stage algorithm for car and truck detection from a moving vehicle. The algorithm performs a search for pertinent features in three dimensions, guided by a ground plane and lane boundary estimation sub-system, and assembles these features into vehicle hypotheses. A number of classifiers are applied to the hypotheses in order to remove false detections. Quantitative analysis on real-world test data show a detection rate of 99.4% and a false positive rate of 1.77%; a result that compares favourably with other systems in the literature.

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Cite

Text

Southall et al. "Real-Time Vehicle Detection for Highway Driving." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206597

Markdown

[Southall et al. "Real-Time Vehicle Detection for Highway Driving." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/southall2009cvpr-real/) doi:10.1109/CVPR.2009.5206597

BibTeX

@inproceedings{southall2009cvpr-real,
  title     = {{Real-Time Vehicle Detection for Highway Driving}},
  author    = {Southall, Ben and Bansal, Mayank and Eledath, Jayan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {541-548},
  doi       = {10.1109/CVPR.2009.5206597},
  url       = {https://mlanthology.org/cvpr/2009/southall2009cvpr-real/}
}