• Title/Summary/Keyword: Traffic sign recognition

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A Recognition of Traffic Safety Signs Using Japanese Puzzle (Japanese Puzzle을 이용한 교통안전 표지판 인식)

  • Sohn, Young-Sun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.3
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    • pp.416-421
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    • 2008
  • This paper realizes a system that recognizes traffic safety signs by applying the principle used for game in reverse. The game used for this paper is one that expresses the shape of temporary objects intended by the maker when the maker sees the numerical image provided on (x, y) coordinates and then expresses it on the mesh. After separating the traffic safety sign image from the input image, the system is realized by outputting the content of the sign into letters by recognizing the forms and colors constituting the sign using the puzzle game above. Our system has fast process time and better rate of recognition than the existing system with black-and-white image processing and recognition without any penciling progress.

Broken Detection of the Traffic Sign by using the Location Histogram Matching

  • Yang, Liu;Lee, Suk-Hwan;Kwon, Seong-Geun;Moon, Kwang-Seok;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.15 no.3
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    • pp.312-322
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    • 2012
  • The paper presents an approach for recognizing the broken area of the traffic signs. The method is based on the Recognition System for Traffic Signs (RSTS). This paper describes an approach to using the location histogram matching for the broken traffic signs recognition, after the general process of the image detection and image categorization. The recognition proceeds by using the SIFT matching to adjust the acquired image to a standard position, then the histogram bin will be compared preprocessed image with reference image, and finally output the location and percents value of the broken area. And between the processing, some preprocessing like the blurring is added in the paper to improve the performance. And after the reorganization, the program can operate with the GPS for traffic signs maintenance. Experimental results verified that our scheme have a relatively high recognition rate and a good performance in general situation.

Improved Environment Recognition Algorithms for Autonomous Vehicle Control (자율주행 제어를 위한 향상된 주변환경 인식 알고리즘)

  • Bae, Inhwan;Kim, Yeounghoo;Kim, Taekyung;Oh, Minho;Ju, Hyunsu;Kim, Seulki;Shin, Gwanjun;Yoon, Sunjae;Lee, Chaejin;Lim, Yongseob;Choi, Gyeungho
    • Journal of Auto-vehicle Safety Association
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    • v.11 no.2
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    • pp.35-43
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    • 2019
  • This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

Impact of Visual Performance on Recognition of Road and Traffic Sign (도로명판 및 교통표지판 인지에 미치는 시기능의 영향)

  • Chu, Byeong-Seon
    • Journal of Korean Society of Transportation
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    • v.29 no.1
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    • pp.47-55
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    • 2011
  • The purpose of this study was to determine the legibility distance for traffic road sign and traffic sign, fixation duration and number of fixation during the time of recognition of traffic road signs under different vision conditions. This experiment was conducted on a closed-road circuit which has realistic driving road and environment Each participant drove the real vehicle for the experiment and specially built traffic road sign for the experiment and traffic road signs on the side of closed-road circuit were used. Different vision conditions were simulated using spectacle lenses to reach visual acuity 1.0 and 0.8 and it was 1.2 without spectacles and each participant tested under 3 vision conditions.. The result of this study demonstrated that there was a significant difference on legibility distance between visual acuity of 1.2 and 0.8 and there were also significant difference on fixation duration and number of fixations with smaller traffic signs. This study demonstrated the importance of vision correction for driving at night-time, also showed there would be difference on legibility distance and efficiency of eye movement such as fixation duration and number of fixation despite of satisfied visual acuity for driver's license requirement.

An Illumination Invariant Traffic Sign Recognition in the Driving Environment for Intelligence Vehicles (지능형 자동차를 위한 조명 변화에 강인한 도로표지판 검출 및 인식)

  • Lee, Taewoo;Lim, Kwangyong;Bae, Guntae;Byun, Hyeran;Choi, Yeongwoo
    • Journal of KIISE
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    • v.42 no.2
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    • pp.203-212
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    • 2015
  • This paper proposes a traffic sign recognition method in real road environments. The video stream in driving environments has two different characteristics compared to a general object video stream. First, the number of traffic sign types is limited and their shapes are mostly simple. Second, the camera cannot take clear pictures in the road scenes since there are many illumination changes and weather conditions are continuously changing. In this paper, we improve a modified census transform(MCT) to extract features effectively from the road scenes that have many illumination changes. The extracted features are collected by histograms and are transformed by the dense descriptors into very high dimensional vectors. Then, the high dimensional descriptors are encoded into a low dimensional feature vector by Fisher-vector coding and Gaussian Mixture Model. The proposed method shows illumination invariant detection and recognition, and the performance is sufficient to detect and recognize traffic signs in real-time with high accuracy.

Speed Sign Recognition Using Sequential Cascade AdaBoost Classifier with Color Features

  • Kwon, Oh-Seol
    • Journal of Multimedia Information System
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    • v.6 no.4
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    • pp.185-190
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    • 2019
  • For future autonomous cars, it is necessary to recognize various surrounding environments such as lanes, traffic lights, and vehicles. This paper presents a method of speed sign recognition from a single image in automatic driving assistance systems. The detection step with the proposed method emphasizes the color attributes in modified YUV color space because speed sign area is affected by color. The proposed method is further improved by extracting the digits from the highlighted circle region. A sequential cascade AdaBoost classifier is then used in the recognition step for real-time processing. Experimental results show the performance of the proposed algorithm is superior to that of conventional algorithms for various speed signs and real-world conditions.

An Vision System for Traffic sign Recognition (교통표지판 인식을 위한 비젼시스템)

  • Kim, Tae-Woo;Kang, Yong-Seok;Cha, Sam;Bae, Cheol-Soo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.2 no.2
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    • pp.45-50
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    • 2009
  • This paper presents an active vision system for on-line traffic sign recognition. The system is composed of two cameras, one is equipped with a wide-angle lens and the other with a telephoto lends, and a PC with an image processing board. The system first detects candidates for traffic signs in the wide-angle image using color, intensity, and shape information. For each candidate, the telephoto-camera is directed to its predicted position to capture the candidate in a large size in the image. The recognition algorithm is designed by intensively using built in functions of an off-the-shelf image processing board to realize both easy implementation and fast recognition. The results of on-road experiments show the feasibility of the system.

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Real-time Identification of Traffic Light and Road Sign for the Next Generation Video-Based Navigation System (차세대 실감 내비게이션을 위한 실시간 신호등 및 표지판 객체 인식)

  • Kim, Yong-Kwon;Lee, Ki-Sung;Cho, Seong-Ik;Park, Jeong-Ho;Choi, Kyoung-Ho
    • Journal of Korea Spatial Information System Society
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    • v.10 no.2
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    • pp.13-24
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    • 2008
  • A next generation video based car navigation is researched to supplement the drawbacks of existed 2D based navigation and to provide the various services for safety driving. The components of this navigation system could be a load object database, identification module for load lines, and crossroad identification module, etc. In this paper, we proposed the traffic lights and road sign recognition method which can be effectively exploited for crossroad recognition in video-based car navigation systems. The method uses object color information and other spatial features in the video image. The results show average 90% recognition rate from 30m to 60m distance for traffic lights and 97% at 40-90m distance for load sign. The algorithm also achieves 46msec/frame processing time which also indicates the appropriateness of the algorithm in real-time processing.

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Segmentation and Recognition of Traffic Signs using Shape Information and Edge Image in Real Image (실영상에서 형태 정보와 에지 영상을 이용한 교통 표지판 영역 추출과 인식)

  • Kwak, Hyun-Wook;Oh,Jun-Taek;Kim, Wook-Hyun
    • The KIPS Transactions:PartB
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    • v.11B no.2
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    • pp.149-158
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    • 2004
  • This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.

Study of Traffic Sign Auto-Recognition (교통 표지판 자동 인식에 관한 연구)

  • Kwon, Mann-Jun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.15 no.9
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    • pp.5446-5451
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    • 2014
  • Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.