70 research outputs found
Atrial fibrillation detection by heart rate variability in Poincare plot
© 2009 Park et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens
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
Deformable Object Tracking Using Clustering and Particle Filter
Visual tracking of a deformable object is a challenging problem, as the target object frequently changes its attributes like shape, posture, color and so on. In this work, we propose a model-free tracker using clustering to track a target object which poses deformations and rotations. Clustering is applied to segment the tracked object into several independent components and the discriminative parts are tracked to locate the object. The proposed technique segments the target object into independent components using data clustering techniques and then tracks by finding corresponding clusters. Particle filters method is incorporated to improve the accuracy of the proposed technique. Experiments are carried out with several standard data sets, and results demonstrate comparable performance to the state-of-the-art visual tracking methods
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