Video-based self-positioning for intelligent transportation systems applications

Parag S. Chandakkar, Ragav Venkatesan, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages718-729
Number of pages12
Volume8887
ISBN (Print)9783319142487
StatePublished - 2014
Event10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States
Duration: Dec 8 2014Dec 10 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8887
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Symposium on Visual Computing, ISVC 2014
CountryUnited States
CityLas Vegas
Period12/8/1412/10/14

Fingerprint

Intelligent Transportation Systems
Positioning
Traffic Management
Traffic Congestion
Traffic congestion
Urban Areas
Congestion
Pricing
Camera
Semantics
Cameras
Face
Model
Angle
Formulation
Prediction
Costs

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chandakkar, P. S., Venkatesan, R., & Li, B. (2014). Video-based self-positioning for intelligent transportation systems applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8887, pp. 718-729). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8887). Springer Verlag.

Video-based self-positioning for intelligent transportation systems applications. / Chandakkar, Parag S.; Venkatesan, Ragav; Li, Baoxin.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8887 Springer Verlag, 2014. p. 718-729 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8887).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chandakkar, PS, Venkatesan, R & Li, B 2014, Video-based self-positioning for intelligent transportation systems applications. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8887, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8887, Springer Verlag, pp. 718-729, 10th International Symposium on Visual Computing, ISVC 2014, Las Vegas, United States, 12/8/14.
Chandakkar PS, Venkatesan R, Li B. Video-based self-positioning for intelligent transportation systems applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8887. Springer Verlag. 2014. p. 718-729. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Chandakkar, Parag S. ; Venkatesan, Ragav ; Li, Baoxin. / Video-based self-positioning for intelligent transportation systems applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8887 Springer Verlag, 2014. pp. 718-729 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@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",
language = "English (US)",
isbn = "9783319142487",
volume = "8887",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "718--729",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Video-based self-positioning for intelligent transportation systems applications

AU - Chandakkar, Parag S.

AU - Venkatesan, Ragav

AU - Li, Baoxin

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84916607154&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84916607154&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9783319142487

VL - 8887

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 718

EP - 729

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -