Wyniki 1-3 spośród 3 dla zapytania: authorDesc:"Safat B. WALI"

Comparative Survey on Traffic Sign Detection and Recognition: a Review DOI:10.15199/48.2015.12.08

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Developing real-time Advanced Driver Assistance Systems (ADAS) based on video aiming to extract reliable vehicle state information has attracted a lot of attention during the past decades. This ADAS system includes inter-vehicle communication, driver behavioral monitoring, and human-machine interactions. In these systems, robust and reliable traffic sign detection and recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. Our main focus is to present the current trends and challenges in the field of developing an efficient TSDR system followed by a detail comparative study between different renowned methods used by various researchers. Finally, conclusion followed by some future suggestion is provided to develop an efficient TSDR system is provided. This survey will hopefully lead to develop an effective traffic sign detection and recognition system which will ensure driver safety in future. Streszczenie. System ADAS (Advanced Driver Assistance System) obejmuje także metody rozpoznawania znaków drogowych. W artykule przedstawiono przegląd metod detekcji i rozpoznawania znaków drogowych bazujących na obrazie video. W artykule dokonano oceny istniejących metod oraz zaproponowano środki poprawy ich efektywności. Studium porównawcze metod detekcji i rozpoznawania znaków drogowych Keywords: Traffic signs, detection, recognition, Advance Driver Assistance System, current issues and challenges Słowa kluczowe: rozpoznawanie znaków drogowych, system ADAS Introduction Automatic traffic sign detection and recognition (TSDR) is an important research in the field of ADAS. Traffic sign has provide important visual information such as; driving on proper lanes, speed limitation, avoiding obstacles, lanes for pedestrians’, direction of destination, roadway access, current traffic condition etc. to help the[...]

Traffic Sign Classification based on Neural Network for Advance Driver Assistance System DOI:10.12915/pe.2014.11.44

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Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application. Streszczenie. W artykule opisano metodę automatycznego rozpoznawania I klasyfikacji znaków drogowych z przenaczeniem do inteligentnych systemów wspomagania kierowcy ADAS. Do tego celu wykorzystano sieci neuronowe przeprowadzając normalizację obrazu, ekstrakcję cech i klasyfikację. Osiągnie™o dokładność rozpoznawania rzędu 84% przy przeciętnym czasie rozpoznawania około 0.13 s. Rozpoznawanie i klasyfikacja znaków drogowych z wykorzystaniem sieci neuronowych Keyword: Traffic sign classification; advance driver assistance system; image normalization; neural network. Słowa kluczowe: rozpoznmawanie znaków drogowych, sieci neuronowe, system ADAS doi:10.12915/pe.2014.11.44 Introduction Traffic sign and classification is part of the Traffic Sign Recognition (TSR) system and it is one of the developed systems under Advance Driver Assistance (ADAS) which help to improve the safety issue on the road. In this era of industrialization, the gradual increase of roads and traffic occurs and thus the number of traffic ac[...]

Shape Matching and Color Segmentation Based Traffic Sign Detection System DOI:10.15199/48.2015.01.06

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An automatic traffic sign detection system detects traffic signs from within images captured by an imaging sensor, and assists the driver to properly operate the vehicle. The idea presented here is through pixel value detection for hazard traffic signs containing red color background, computing in range regions and finally shape matching to choose the most appropriate traffic sign candidates to be drawn on the screen. The experimental result showed that, by comparing with the similar color segmentation based techniques, the proposed system has a higher accuracy of traffic sign detection rate with a lower computational time. Streszczenie. W artykule opisano system automatycznego rozpoznawania znaków drogowych na podstawie sygnału czujnika obrazu. System rozpoznaje znaki na czerwonym tle, dopasowuje odpowiedni znak i wyświetla go na ekranie. Automatyczny system rozpoznawania znaków drogowych bazujący na dopasowaniu kształtu i segmentacji koloru. Keyword: Traffic sign detection; segmentation; shape matching; region detection. Słowa kluczowe: rozpoznawanie znaków drogowych, dopasowanie kształtu, segmentacja koloru Introduction As the transportation system develops, the people give more attention to the safety issues of driving. For that reason the topic, automatic Traffic Sign Detection (TSD) system is becoming more popular among the researchers in recent years. It achieves one important application for advance driver assistance systems (ADAS). The traffic signs give us important information about the o way traffic for guiding the vehicle while moving in the street. In an adverse traffic condition, driver may not notice traffic signs may cause accident and that time traffic sign recognition (TSR) system come into action. Thus, the TSR system makes the driving safer and easier. Developing a TSR system is a tedious job as the traffic sign is keep changing. There are some important issues such as; lighting condition differs according to t[...]

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