238 research outputs found
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on
Neural Networks (IJCNN) 201
Z_2-Regge versus Standard Regge Calculus in two dimensions
We consider two versions of quantum Regge calculus. The Standard Regge
Calculus where the quadratic link lengths of the simplicial manifold vary
continuously and the Z_2-Regge Model where they are restricted to two possible
values. The goal is to determine whether the computationally more easily
accessible Z_2 model still retains the universal characteristics of standard
Regge theory in two dimensions. In order to compare observables such as average
curvature or Liouville field susceptibility, we use in both models the same
functional integration measure, which is chosen to render the Z_2-Regge Model
particularly simple. Expectation values are computed numerically and agree
qualitatively for positive bare couplings. The phase transition within the
Z_2-Regge Model is analyzed by mean-field theory.Comment: 21 pages, 16 ps-figures, to be published in Phys. Rev.
Leveraging Model Fusion for Improved License Plate Recognition
License Plate Recognition (LPR) plays a critical role in various
applications, such as toll collection, parking management, and traffic law
enforcement. Although LPR has witnessed significant advancements through the
development of deep learning, there has been a noticeable lack of studies
exploring the potential improvements in results by fusing the outputs from
multiple recognition models. This research aims to fill this gap by
investigating the combination of up to 12 different models using
straightforward approaches, such as selecting the most confident prediction or
employing majority vote-based strategies. Our experiments encompass a wide
range of datasets, revealing substantial benefits of fusion approaches in both
intra- and cross-dataset setups. Essentially, fusing multiple models reduces
considerably the likelihood of obtaining subpar performance on a particular
dataset/scenario. We also found that combining models based on their speed is
an appealing approach. Specifically, for applications where the recognition
task can tolerate some additional time, though not excessively, an effective
strategy is to combine 4-6 models. These models may not be the most accurate
individually, but their fusion strikes an optimal balance between accuracy and
speed.Comment: Accepted for presentation at the Iberoamerican Congress on Pattern
Recognition (CIARP) 202
Measuring the string susceptibility in 2D simplicial quantum gravity using the Regge approach
We use Monte Carlo simulations to study pure 2D Euclidean quantum gravity
with -interaction on spherical topologies, employing Regge's formulation.
We attempt to measure the string susceptibility exponent by
using a finite-size scaling Ansatz in the expectation value of , as has
been done in a previous study by Bock and Vink ( hep-lat/9406018 ). By
considerably extending the range and statistics of their study we find that
this Ansatz is plagued by large systematic errors. The specific string
susceptibility exponent \GS' is found to agree with theoretical predictions,
but its determination also is subject to large systematic errors and the
presence of finite-size scaling corrections. To circumvent this obstacle we
suggest a new scaling Ansatz which in principle should be able to predict both,
\GS and \GS'. First results indicate that this requires large system sizes
to reduce the uncertainties in the finite-size scaling Ans\"atze. Nevertheless,
our investigation shows that within the achievable accuracy the numerical
estimates are still compatible with analytic predictions, contrary to the
recent claim by Bock and Vink.Comment: 33 pages, self unpacking uuencoded PostScript file, including all the
figures. Paper also available at http://www.physik.fu-berlin.de/~holm
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