Title: Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification,
Abstract: Monocular camera systems are prevailing in intelligent transportation
systems, but by far they have rarely been used for dimensional purposes such as
to accurately estimate the localization information of a vehicle. In this
paper, we show that this capability can be realized. By integrating a series of
advanced computer vision techniques including foreground extraction, edge and
line detection, etc., and by utilizing deep learning networks for fine-grained
vehicle model classification, we developed an algorithm which can estimate
vehicles location (position, orientation and boundaries) within the environment
down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy.
With this enhancement, current massive surveillance camera systems can
potentially play the role of e-traffic police and trigger many new intelligent
transportation applications, for example, to guide vehicles for parking or even
for autonomous driving.