Title: Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection,
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.