IMM/EKF filter based classification of real-time freeway video traffic without learning
This paper addresses the problem of traffic variable estimation and traffic state classification of highway traffic, from video. To solve this problem, the authors propose to use the Interactive Multiple Model (IMM) filter with a multi-class macroscopic model. This filter runs two Extended Kalman Filters (EKF) to smooth the measured traffic parameters. In addition, the models’ probabilities that it provides are exploited to simply classify the traffic state as either free or congested, without the need for a training phase. The evaluation of the proposed system using simulated traffic parameters shows that it achieves a very accurate traffic state classification. The system was also tested in the real world, using video data acquired on a freeway by camera sensors. The obtained classification rates are comparable to those obtained by SVM classification, but at a significantly lower computational load.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/19427867
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Supplemental Notes:
- © 2021 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Ouessai, Asmâa
- Keche, Mokhtar
- Publication Date: 2022-7
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 610-621
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Serial:
- Transportation Letters: The International Journal of Transportation Research
- Volume: 14
- Issue Number: 6
- Publisher: Taylor & Francis
- ISSN: 1942-7867
- EISSN: 1942-7875
- Serial URL: http://www.tandfonline.com/toc/ytrl20/current
Subject/Index Terms
- TRT Terms: Classification; Freeways; Kalman filtering; Machine learning; Real time information; Traffic estimation; Traffic surveillance
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01854435
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 10 2022 4:39PM