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 language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 718-729 |
| Number of pages | 12 |
| Volume | 8887 |
| ISBN (Print) | 9783319142487 |
| State | Published - 2014 |
| Event | 10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States Duration: Dec 8 2014 → Dec 10 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8887 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 10th International Symposium on Visual Computing, ISVC 2014 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 12/8/14 → 12/10/14 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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 proceeding › Conference contribution
}
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 -