4,886 research outputs found
Empirical Study of Car License Plates Recognition
The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.Comment: 7 pages, 5 figures.This work has been accepted by ICRA 2019. The demo
video can be found at https://youtu.be/Bkt53dAehj
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
Research of Indonesian license plates recognition on moving vehicles
The recognition of the characters in the license plate has been widely studied, but research to recognize the character of the license plate on a moving car is still rarely studied. License plate recognition on a moving car has several difficulties, for example capturing still images on moving images with non-blurred results. In addition, there are also several problems such as environmental disturbances (low lighting levels and heavy rain). In this study, a novel framework for recognizing license plate numbers is proposed that can overcome these problems. The proposed method in this study: detects moving vehicles, judges the existence of moving vehicles, captures moving vehicle images, deblurring images, locates license plates, extracts vertical edges, removes unnecessary edge lines, segments license plate locations, Indonesian license plate cutting character segmenting, character recognition. Experiments were carried out under several conditions: suitable conditions, poor lighting conditions (dawn, evening, and night), and unfavourable weather conditions (heavy rain, moderate rain, and light rain). In the experiment to test the success of the license plate number recognition, it was seen that the proposed method succeeded in recognizing 98.1 % of the total images tested. In unfavorable conditions such as poor lighting or when there are many disturbances such as rain, there is a decrease in the success rate of license plate recognition. Still, the proposed method's experimental results were higher than the method without deblurring by 1.7 %. There is still unsuccessful in recognizing license plates from the whole experiment due to a lot of noise. The noise can occur due to unfavourable environmental conditions such as heavy rain
Highly accurate texture-based vehicle segmentation method
In modern traffic surveillance, computer vision methods have often been employed to detect vehicles of interest because of the rich information content contained in an image. Segmentation of moving vehicles using image processing and analysis algorithms has been an important research topic in the past decade. However, segmentation results are strongly affected by two issues: moving cast shadows and reflective regions, both of which reduce accuracy and require postprocessing to alleviate the degradation. We propose an efficient and highly accurate texture-based method for extracting the boundary of vehicles from the stationary background that is free from the effect of moving cast shadows and reflective regions. The segmentation method utilizes the differences in textural property between the road, vehicle cast shadow, reflection on the vehicle, and the vehicle itself, rather than just the intensity differences between them. By further combining the luminance and chrominance properties into an OR map, a number of foreground vehicle masks are constructed through a series of morphological operations, where each mask describes the outline of a moving vehicle. The proposed method has been tested on real-world traffic image sequences and achieved an average error rate of 3.44% for 50 tested vehicle images. © 2004 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio
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