Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification
This enables existing surveillance camera systems to function as e-traffic police and support applications like parking guidance and autonomous driving, representing an incremental improvement in intelligent transportation.
The paper tackled the problem of accurately estimating vehicle localization using monocular cameras by integrating computer vision techniques and deep learning for fine-grained classification, achieving 3.79% position accuracy and 2.5 degrees orientation accuracy.
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.