Latest revision as of 03:33, 2 February 2021

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.

Comment: WACV 2015

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1704.01256

• http://arxiv.org/pdf/1704.01256

• http://xplorestaging.ieee.org/ielx7/7045624/7045853/07045914.pdf?arnumber=7045914,

http://dx.doi.org/10.1109/wacv.2015.60

• https://dblp.uni-trier.de/db/journals/corr/corr1704.html#ChandakkarWL17,

http://yilinwang.org/papers/midas.pdf,

http://ui.adsabs.harvard.edu/abs/2017arXiv170401256C/abstract,

https://arxiv.org/abs/1704.01256,

https://asu.pure.elsevier.com/en/publications/improving-vision-based-self-positioning-in-intelligent-transporta,

http://dx.doi.org/10.1109/WACV.2015.60,

https://academic.microsoft.com/#/detail/2165481052