The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Traffic monitoring and accident detection at intersections
Abstract
We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Recognizing human action in time-sequential images using hidden Markov model
Yamato, J., Ohya, J., & Ishii, K. (2003) · Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1992.223161 · Cited by 1,273
Cited by
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
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197
Hierarchical and Networked Vehicle Surveillance in ITS: A Survey
Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2552778 · Cited by 160
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
Metadata
- Authors
- Kamijo, S., Matsushita, Y., Ikeuchi, K., & Sakauchi, M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2000-06
- Volume
- 1
- Issue
- 2
- Pages
- 108-118
- ISSN
- 1524-9050 (ISSN-L)
- OpenAlex
- W2156017603
- Cited by
- 536
- References
- 2
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kamijo, S., Matsushita, Y., Ikeuchi, K., & Sakauchi, M. (2000). Traffic monitoring and accident detection at intersections. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/6979.880968
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