8,469 research outputs found
Overview of Environment Perception for Intelligent Vehicles
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The
state-of-the-art algorithms and modeling methods for intelligent
vehicles are given, with a summary of their pros and cons. A
special attention is paid to methods for lane and road detection,
traffic sign recognition, vehicle tracking, behavior analysis, and
scene understanding. In addition, we provide information about
datasets, common performance analysis, and perspectives on
future research directions in this area
Automatically detecting road sign text from natural scene video
Automatic detection of text on road signs can help drivers keep aware of the traffic situation and surrounding environments by reminding them of the signs ahead. Current systems can only detect constrained road signs or produce unsatisfying performance when dealing with complex scenes in practical use. This paper firstly reviews the existing techniques used for text detection from natural scene. A novel system which detects text on road signs from natural scene video is then proposed. Our detailed approaches and methodology give a promising solution to this problem in order to reduce the running time and improve the recognition rate. © 2006 IEEE
Automatic Signboard Recognition in Low Quality Night Images
An essential requirement for driver assistance systems and autonomous driving
technology is implementing a robust system for detecting and recognizing
traffic signs. This system enables the vehicle to autonomously analyze the
environment and make appropriate decisions regarding its movement, even when
operating at higher frame rates. However, traffic sign images captured in
inadequate lighting and adverse weather conditions are poorly visible, blurred,
faded, and damaged. Consequently, the recognition of traffic signs in such
circumstances becomes inherently difficult. This paper addressed the challenges
of recognizing traffic signs from images captured in low light, noise, and
blurriness. To achieve this goal, a two-step methodology has been employed. The
first step involves enhancing traffic sign images by applying a modified MIRNet
model and producing enhanced images. In the second step, the Yolov4 model
recognizes the traffic signs in an unconstrained environment. The proposed
method has achieved 5.40% increment in [email protected] for low quality images on
Yolov4. The overall [email protected] of 96.75% has been achieved on the GTSRB dataset.
It has also attained [email protected] of 100% on the GTSDB dataset for the broad
categories, comparable with the state-of-the-art work.Comment: 13 pages, CVIP 202
Road traffic sign detection and classification
A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown.Publicad
Generic object classification for autonomous robots
Un dels principals problemes de la interacció dels robots autònoms és el coneixement de l'escena. El reconeixement és fonamental per a solucionar aquest problema i permetre als robots interactuar en un escenari no controlat. En aquest document presentem una aplicació prà ctica de la captura d'objectes, de la normalització i de la classificació de senyals triangulars i circulars. El sistema s'introdueix en el robot Aibo de Sony per a millorar-ne la interacció. La metodologia presentada s'ha comprobat en simulacions i problemes de categorització reals, com ara la classificació de senyals de trà nsit, amb resultats molt prometedors.Uno de los principales problemas de la interacción de los robots autónomos es el conocimiento de la escena. El reconocimiento es fundamental para solventar este problema y permitir a los robots interactuar en un escenario no controlado. En este documento, presentamos una aplicación práctica de captura del objeto, normalización y clasificación de señales triangulares y circulares. El sistema es introducido en el robot Aibo de Sony para mejorar el comportamiento de la interacción del robot. La metodologÃa presentada ha sido testeada en simulaciones y problemas de categorización reales, como es la clasificación de señales de tráfico, con resultados muy prometedores.One of the main problems of autonomous robots interaction is the scene knowledge. Recognition is concerned to deal with this problem and to allow robots to interact in uncontrolled environments. In this paper, we present a practical application for object fitting, normalization and classification of triangular and circular signs. The system is introduced in the Aibo robot of Sony to increase the robot interaction behaviour. The presented methodology has been tested in real simulations and categorization problems, as the traffic signs classification, with very promising results.Nota: Aquest document conté originà riament altre material i/o programari només consultable a la Biblioteca de Ciència i Tecnologia
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