Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection

Parag S. Chandakkar, Yilin Wang, Baoxin Li

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

8 Citations (Scopus)

Abstract

Traffic congestion is a widespread problem. Dynamic traffic routing systems and congestion pricing are getting importance in recent research. Lane prediction and vehicle density estimation is an important component of such systems. We introduce a novel problem of vehicle self positioning which involves predicting the number of lanes on the road and vehicle's position in those lanes using videos captured by a dashboard camera. We propose an integrated closed-loop approach where we use the presence of vehicles to aid the task of self-positioning and vice versa. To incorporate multiple factors and high-level semantic knowledge into the solution, we formulate this problem as a Bayesian framework. In the framework, the number of lanes, the vehicle's position in those lanes and the presence of other vehicles are considered as parameters. We also propose a bounding box selection scheme to reduce the number of false detections and increase the computational efficiency. We show that the number of box proposals decreases by a factor of 6 using the selection approach. It also results in large reduction in the number of false detections. The entire approach is tested on real-world videos and is found to give acceptable results.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages404-411
Number of pages8
ISBN (Print)9781479966820
DOIs
StatePublished - Feb 19 2015
Event2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015 - Waikoloa, United States
Duration: Jan 5 2015Jan 9 2015

Other

Other2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015
CountryUnited States
CityWaikoloa
Period1/5/151/9/15

Fingerprint

Traffic congestion
Computational efficiency
Semantics
Cameras
Costs

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Chandakkar, P. S., Wang, Y., & Li, B. (2015). Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 (pp. 404-411). [7045914] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2015.60

Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection. / Chandakkar, Parag S.; Wang, Yilin; Li, Baoxin.

Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 404-411 7045914.

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

Chandakkar, PS, Wang, Y & Li, B 2015, Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection. in Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015., 7045914, Institute of Electrical and Electronics Engineers Inc., pp. 404-411, 2015 15th IEEE Winter Conference on Applications of Computer Vision, WACV 2015, Waikoloa, United States, 1/5/15. https://doi.org/10.1109/WACV.2015.60
Chandakkar PS, Wang Y, Li B. Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 404-411. 7045914 https://doi.org/10.1109/WACV.2015.60
Chandakkar, Parag S. ; Wang, Yilin ; Li, Baoxin. / Improving vision-based self-positioning in intelligent transportation systems via integrated lane and vehicle detection. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 404-411
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