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International Conference on Intelligent Systems and Computer Vision (ISCV)

作者： Bouti, Amal Mahraz, Mohamed Adnane Riffi, Jamal Tairi, Hamid USMBA FSDM Dept Informat LIIAN Lab Fes Morocco

ISBN: (纸本)9781538643969

Extracting the contents of a digital image has been proven a hard problem for computers. Since for them, an image is only a matrix of values, knowing what structures a human would recognize in this image, is a nontrivial problem. In this paper, we have implemented and tested a system of detection of road signs. The approach taken in this work consists of using convolutional neural network where this network is supposed to distinguish between different classes of signs (stop, attention etc.) and the final model will then be integrated to the autonomous cars. Tests carried out on the dataset GTSRB (The German Traffic sign recognition Benchmark) shows the performance of the system currently being developed.

关键词： road sign recognition Convolutional Neural Network Neural Network GTSRB

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A Detailed Study on Bangladeshi road sign Detection and recognition 4

A Detailed Study on Bangladeshi Road Sign Detection and Reco...

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4th International Conference on Electrical Information and Communication Technology (EICT)

作者： Ahsan, Sk Md Masudul Das, Sunanda Kumar, Shanto La Tasriba, Zannati Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781728160405

Automatic road sign detection and recognition is crucial for autonomous Driver Assistance Systems (DAS). Numerous road accidents and casualties are reported every year due to the negligence of drivers. The system is mainly developed for autonomous vehicles and drives to warn them about the existing road sign on the road and also to reduce the frequency of accidents throughout the country. road sign detection and recognition is still challenging concerning Bangladesh because of the shortage of big dataset. The dataset for this work has been collected from different regions throughout the country. In this system, the Single Shot Multibox Detector (SSD) is used for automatic detection and recognition of road sign. The Convolutional Neural Network (CNN) is also applied for the recognition purpose. The system is able to achieve the average detection rate of 76.52% and the recognition accuracy of 86.23% using SSD and also accomplish the recognition accuracy of 80.26% while using CNN. Therefore, the proposed system is robust enough to detect and recognize road sign.

关键词： road sign Detection road sign recognition Single Shot Multibox Detector (SSD) Convolutional Neural Network (CNN)

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Low Latency Approach in road sign recognition and Tracking for Autonomous Vehicles

Low Latency Approach in Road Sign Recognition and Tracking f...

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International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

作者： Pitawelayalage Dasun Dileepa Pitawela Lochandaka Ranathunga Department of Information Technology University of Moratuwa Katubedda Sri Lanka

ISBN: (纸本)9781538680988;9781538680971

In autonomous vehicle routing real-time road sign recognition and tracking are complex tasks. This article was targeted on exploring an approach for road sign detection, recognition and tracking in real time with reduced computational complexities and enhanced efficiency. Due to various sophisticated technologies investigated in recent research approaches of autonomous vehicle routings, computational complexity and memory requirements getting continuously higher. Hence, by reducing a significant amount of CPU burden and memory from vision processing has the potential of boosting the performance as a whole. Research was conducted proposing a novel approach for identification and recognizing of signs with a domain-specific data augmentation technique and with use of relatively small-scale convolutional image classifier. As the obtained results were promising, it shows the potential of scalability with a higher degree of signs classes.

关键词： road sign recognition Computer vision Convolutional neural networks Object classification Object tracking

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Development of road sign recognition for ADAS Using OpenCV

Development of Road Sign Recognition for ADAS Using OpenCV

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International Conference on Intelligent Computing and Control (I2C2)

作者： Botekar, Naina P. Mahalakshmi, M. N. RV Coll Engn Dept Telecommun Engn Bengaluru India

ISBN: (纸本)9781538603741

Real time road sign recognition technology of advanced driver assistance systems (ADAS) provide necessary information and instructions to help the driver to drive safely. road sign recognition is the technology of driver assistance system which interprets the signs to the driver. recognition is dependent on the combination of detection and classification. Among the various available methods the most efficient one is chosen. Thus detection of region of interest is performed by using Histogram of oriented gradient and classification by using support vector machine. Training data is generated from our own database. This paper represents a study to recognize road signs using OpenCv techniques. This is implemented in visual studio and ported on NVIDIA's TK1 platform. The experimental results shows good performance for recognition of ideogram based signs with an average speed of 25 frames per second having accuracy up to 94%.

关键词： Hough Transform (HT) histogram of oriented gradient (HOG) road sign recognition region of interest (ROI) support vector machine (SVM)

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Bangladeshi road sign recognition Based on DtBs Vector and Artificial Neural Network

Bangladeshi Road Sign Recognition Based on DtBs Vector and A...

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International Conference on Electrical, Computer and Communication Engineering (ECCE)

作者： Chakraborty, Soumen Uddin, Mohammed Nasir Deb, Kaushik Chittagong Univ Engn & Technol Dept CSE Chittagong Bangladesh Jagannath Univ Dept CSE Dhaka Bangladesh

ISBN: (纸本)9781509056279

road sign recognition from road image is a crucial area of intelligent transportation system which role is to raise awareness of drivers, pedestrians and automated driving system. In this regard, a framework has been proposed in this paper for recognizing Bangladeshi road sign (BRS). For detecting the road sign (RS), two natural properties of a BRS is utilized, they are border color rim and shape of the RS. Secondly distance to borders (DtBs) vector is used for shape verification of BRS. For recognizing those candidate regions artificial neural network (ANN) has performed. Based on these ideas initially, YCbCr color model is used to eliminate the illumination sensitiveness and statistical threshold value is used for color segmentation. Next, labeling and filtering is used to extract the shapes of candidate region. DtBs vector is used to verify the region of interest (ROI) of BRS. Affine transformation is used for triangular and quadrangular region to avoid sheared road sign (RS) blob. Finally, ANN is used to recognize the BRS blob. Various road signs are utilized to test the proposed framework under various conditions and results are presented to verify its efficiency.

关键词： YCbCr color model road sign detection road sign recognition DtB vector Traffic sign

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road sign recognition System on Raspberry Pi

Road Sign Recognition System on Raspberry Pi

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IEEE Long Island Systems, Applications and Technology Conference (LISAT)

作者： Bilgin, Enis Robila, Stefan Montclair State Univ Dept Comp Sci Montclair NJ 07043 USA

ISBN: (纸本)9781467384902

Digital image processing, i.e. the use of computer systems to process pictures, has applications in many fields, including of medicine, space exploration, geology and oceanography and continues to increase in its applicability. The main objective of this paper is to demonstrate the ability of image processing algorithms on a small computing platform. Specifically we created a road sign recognition system based on an embedded system that reads and recognizes speed signs. The paper describes the characteristics of speed signs, requirements and difficulties behind implementing a real-time base system with embedded system, and how to deal with numbers using image processing techniques based on shape and dimension analysis. The paper also shows the techniques used for classification and recognition. Color analysis also plays a specifically important role in many other different applications for road sign detection, this paper points to many problems regarding stability of color detection due to daylight conditions, so absence of color model can led a better solution. In this project lightweight techniques were mainly used due to limitation of real-time based application and Raspberry Pi capabilities. Raspberry Pi is the main target for the implementation, as it provides an interface between sensors, database, and image processing results, while also performing functions to manipulate peripheral units (usb dongle, keyboard etc.).

关键词： component Digital Image Processing Raspberry Pi Embedded System road sign recognition

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road sign recognition using VIAPIX® module and Correlation 26

Road Sign Recognition using VIAPIX® module and Correlation

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Conference on Optical Pattern recognition XXVI

作者： Ouerhani, Y. Desthieux, M. Alfalou, A. Actris Brest F-29803 Brest 9 France ISEN Brest L BISEN Equipe Vis F-29228 Brest 2 France

ISBN: (纸本)9781628415933

In this paper, we propose and validate a new system used to explore road assets. In this work we are interested on the vertical road signs. To do this, we are based on the combination of road signs detection, recognition and identification using data provides by sensors. The proposed approach consists on using panoramic views provided by the innovative device, VIAPIX (R)(1), developed by our company ACTRIS(2). We are based also on the optimized correlation technique for road signs recognition and identification on pictures. Obtained results shows the interest on using panoramic views compared to results obtained using images provided using only one camera.

关键词： Correlation road sign recognition VIAPIX HSV

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A new approach in road sign recognition based on fast fractal coding

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NEURAL COMPUTING & APPLICATIONS 2013年第3-4期22卷 615-625页

作者： Pazhoumand-dar, Hossein Yaghoobi, Mahdi Islamic Azad Univ Dept Comp Sci Mashhad Branch Mashhad *** Iran

The tasks of traffic signs are notifying drivers about the current state of the road and giving them other important information for navigation. In this paper, a new approach for detection, tracking, and recognition such objects is presented. road signs are detected using color thresholding, after that candidate blobs that have specific criteria are classified based on their geometrical shape and are tracked trough successive frames based on a new similarity measure. Candidate blobs that successfully pass the tracking module are processed for extracting their fractal features, and final recognition is done based on support vector machines with kernel function. Results validate effectiveness of newly employed fractal feature and show high accuracy with a low false hit rate of this method and its robustness to illumination changes and road sign occlusion or scale changes. Also results indicate that compared to the other pictogram feature representation techniques, this approach shows a more proper description of road signs.

关键词： road sign recognition Computer vision-based driver assistance Fractal feature extraction Support vector machines road sign tracking

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road sign recognition with Fuzzy Adaptive Pre-Processing Models

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SENSORS 2012年第5期12卷 6415-6433页

作者： Lin, Chien-Chuan Wang, Ming-Shi Natl Cheng Kung Univ Taiwan Dept Engn Sci Tainan 701 Taiwan

A road sign recognition system based on adaptive image pre-processing models using two fuzzy inference schemes has been proposed. The first fuzzy inference scheme is to check the changes of the light illumination and rich red color of a frame image by the checking areas. The other is to check the variance of vehicle's speed and angle of steering wheel to select an adaptive size and position of the detection area. The Adaboost classifier was employed to detect the road sign candidates from an image and the support vector machine technique was employed to recognize the content of the road sign candidates. The prohibitory and warning road traffic signs are the processing targets in this research. The detection rate in the detection phase is 97.42%. In the recognition phase, the recognition rate is 93.04%. The total accuracy rate of the system is 92.47%. For video sequences, the best accuracy rate is 90.54%, and the average accuracy rate is 80.17%. The average computing time is 51.86 milliseconds per frame. The proposed system can not only overcome low illumination and rich red color around the road sign problems but also offer high detection rates and high computing performance.

关键词： road sign recognition fuzzy inference Adaboost classifier support vector machine

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Computer Vision Model for Traffic sign recognition and Detection-A Survey

Computer Vision Model for Traffic Sign Recognition and Detec...

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International Conference on Communications and Cyber Physical Engineering (ICCCE)

作者： Sindhu, O. S. S. V. Paul, P. Victer Vignan Fdn Sci Technol & Res Dept Comp Sci & Engn Vadlamudi India

ISBN: (纸本)9789811302121;9789811302114

Computer vision is an interdisciplinary field which deals with a high level understanding of digital videos or images. The result of computer vision is in the form of a decision or data. This also includes methods such as gaining, processing, analyzing, understanding, and extracting high dimensionality data. Object recognition is used for identifying the objects in any image or video. The appearance of objects may vary due to lighting or colors, viewing direction, and size or shape. The problem we identify here is accuracy at nighttime and in certain weather conditions is less that when compared to daytime and also we enable to detect some signs at the night time. In this paper, we present a detailed study of computer vision, object recognition, and also a study of traffic sign detection and recognition along with its applications, advantages, and disadvantages. The study focuses on several subject, e.g., proposal theme, model, performance evaluation, and advantages and disadvantages of the work. The performance evaluation part is further discussed w.r.t. the experimental setup, different existing techniques, and the various performance assessment factors used to justify the proposed model. This study will be useful for researchers looking to obtain substantial knowledge on the current status of traffic sign detection and recognition, and the various existing problems that need to be resolved.

关键词： Computer vision Object recognition Traffic road sign detection road sign recognition MSERs

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