ML Anthology

A Real-Time Computer Vision System for Measuring Traffic Parameters

Philip F. McLauchlan, David Beymer, Benjamin Coifman, Jitendra Malik
CVPR 1997 pp. 495-501
doi:10.1109/CVPR.1997.609371 /cvpr/1997/mclauchlan1997cvpr-real/

Abstract

For the problem of tracking vehicles on freeways using machine vision, existing systems work well in free-flowing traffic. Traffic engineers, however, are more interested in monitoring freeways when there is congestion, and current systems break down for congested traffic due to the problem of partial occlusion. We are developing a feature-based tracking approach for the task of tracking vehicles under congestion. Instead of tracking entire vehicles, vehicle sub-features are tracked to make the system robust to partial occlusion. In order to group together sub-features that come from the same vehicle, the constraint of common motion is used. In this paper we describe the system, a real-time implementation using a network of DSP chips, and experiments of the system on approximately 44 lane hours of video data.

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Cite

Text

McLauchlan et al. "A Real-Time Computer Vision System for Measuring Traffic Parameters." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609371

Markdown

[McLauchlan et al. "A Real-Time Computer Vision System for Measuring Traffic Parameters." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/mclauchlan1997cvpr-real/) doi:10.1109/CVPR.1997.609371

BibTeX

@inproceedings{mclauchlan1997cvpr-real,
  title     = {{A Real-Time Computer Vision System for Measuring Traffic Parameters}},
  author    = {McLauchlan, Philip F. and Beymer, David and Coifman, Benjamin and Malik, Jitendra},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {495-501},
  doi       = {10.1109/CVPR.1997.609371},
  url       = {https://mlanthology.org/cvpr/1997/mclauchlan1997cvpr-real/}
}