7 research outputs found
Unconstrained Road Marking Recognition with Generative Adversarial Networks
Recent road marking recognition has achieved great success in the past few
years along with the rapid development of deep learning. Although considerable
advances have been made, they are often over-dependent on unrepresentative
datasets and constrained conditions. In this paper, to overcome these
drawbacks, we propose an alternative method that achieves higher accuracy and
generates high-quality samples as data augmentation. With the following two
major contributions: 1) The proposed deblurring network can successfully
recover a clean road marking from a blurred one by adopting generative
adversarial networks (GAN). 2) The proposed data augmentation method, based on
mutual information, can preserve and learn semantic context from the given
dataset. We construct and train a class-conditional GAN to increase the size of
training set, which makes it suitable to recognize target. The experimental
results have shown that our proposed framework generates deblurred clean
samples from blurry ones, and outperforms other methods even with unconstrained
road marking datasets.Comment: Accepted at IEEE Intelligent Vehicles Symposium (IV), 201
Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution
Although most current license plate (LP) recognition applications have been
significantly advanced, they are still limited to ideal environments where
training data are carefully annotated with constrained scenes. In this paper,
we propose a novel license plate recognition method to handle unconstrained
real world traffic scenes. To overcome these difficulties, we use adversarial
super-resolution (SR), and one-stage character segmentation and recognition.
Combined with a deep convolutional network based on VGG-net, our method
provides simple but reasonable training procedure. Moreover, we introduce
GIST-LP, a challenging LP dataset where image samples are effectively collected
from unconstrained surveillance scenes. Experimental results on AOLP and
GIST-LP dataset illustrate that our method, without any scene-specific
adaptation, outperforms current LP recognition approaches in accuracy and
provides visual enhancement in our SR results that are easier to understand
than original data.Comment: Accepted at VISAPP, 201
A research on ICT standards management based on standards reusability analysis
Standards are those generally agreed through repetitive use. They also make products and services more reliable. Thus, when developing standards, one should at first be aware of what the market actually needs not what the developers and providers want. If not, unnecessary standards will eventually end up being unused and dismissed. In this sense, standards developed primarily based on users' point of view are much more reusable. As information and communication technology (ICT) being accelerated these days, the number of standardisation activities has been dramatically increased and a great number of group and international standards has been developed all over the world. Therefore, Standards Developing Organisations (SDOs) follow routine maintenance procedures on their developed standards in order to assure their quality. In this paper, the authors propose a methodology for managing developed standards to enhance standards reusability in terms of standards development, maintenance, and management