@inproceedings{1f37dc4b6ca448b99770541977736a37,
title = "Video-based self-positioning for intelligent transportation systems applications",
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-likelihoodestimator making the method tolerant to slight viewing angle variation. The overall approach is tested on real-world videos and is found to be effective.",
author = "Chandakkar, {Parag S.} and Ragav Venkatesan and Baoxin Li",
year = "2014",
month = jan
day = "1",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "718--729",
editor = "Jason Jerald and George Bebis and Bahram Parvin and Zhigang Deng and Richard Boyle and Maha, {El Choubassi} and Hui Zhang and Darko Koracin and Ryan McMahan and Drucker, {Steven M.} and Mark Carlson and Kambhamettu Chandra",
booktitle = "Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings",
note = "10th International Symposium on Visual Computing, ISVC 2014 ; Conference date: 08-12-2014 Through 10-12-2014",
}