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A real-time computer vision system for measuring traffic parameters
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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Cited by
A Trainable System for Object Detection
Papageorgiou, C., & Poggio, T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008162616689 · Cited by 1,289
Automatic Traffic Surveillance System for Vehicle Tracking and Classification
Hsieh, J.-W., Yu, S.-H., Chen, Y.-S., & Hu, W.-F. (2006) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.874722 · Cited by 423
Video processing techniques for traffic flow monitoring: A survey
Tian, B., Yao, Q., Gu, Y., Wang, K., & Li, Y. (2011) · 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2011.6083125 · Cited by 106
Li, J., Xu, Z., Fu, L., Zhou, X., & Yu, H. (2021) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2020.102946 · Cited by 80
Moving Vehicle Detection for Measuring Traffic Count Using OpenCV
Uke, N. J., & Thool, R. C. (2013) · Journal of Automation and Control Engineering · doi:10.12720/joace.1.4.349-352 · Cited by 67
Metadata
- Authors
- Beymer, D., McLauchlan, P., Coifman, B., & Malik, J.
- Venue
- Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-22
- Pages
- 495-501
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2137479423
- Cited by
- 493
- References
- 0
- FWCI
- 19.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Beymer, D., McLauchlan, P., Coifman, B., & Malik, J. (2002). A real-time computer vision system for measuring traffic parameters. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1997.609371
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