Abstract
Many urban areas face traffic congestion. Automatic traffic management systems and congestion pricing are getting prominence in recent research. An important stage in such systems is lane prediction and on-road self-positioning. We introduce a novel problem of vehicle self-positioning which involves predicting the number of lanes on the road and localizing the vehicle within those lanes, using the video captured by a dashboard camera. To overcome the disadvantages of most existing low-level vision-based techniques while tackling this complex problem, we formulate a model in which the video is a key observation. The model consists of the number of lanes and vehicle position in those lanes as parameters, hence allowing the use of high-level semantic knowledge. Under this formulation, we employ a lane-width-based model and a maximum-likelihood-estimator making the method tolerant to slight viewing angle variation. The overall approach is tested on real-world videos and is found to be effective.
Keywords
- Random Forest
- Intelligent Transportation System
- Intelligent Transport System
- Congestion Price
- Lane Marker
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Chandakkar, P.S., Venkatesan, R., Li, B. (2014). Video-Based Self-positioning for Intelligent Transportation Systems Applications. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_69
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DOI: https://doi.org/10.1007/978-3-319-14249-4_69
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14248-7
Online ISBN: 978-3-319-14249-4
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