DETEKSI DAN REKOGNISI RAMBU-RAMBU LALU LINTAS DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

2017 ◽  
Vol 3 (2) ◽  
Author(s):  
Kurniawan Nur Ramadhani ◽  
M.Syahrul Mubarok ◽  
Agnes Dirgahayu Palit
Keyword(s):  
Support Vector Machines ◽  
Road Safety ◽  
Support Vector ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Histogram Of Oriented Gradient ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

[Id]Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score.Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score.[En]The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

Road-Sign Detection and Recognition Based on Support Vector Machines

2007 ◽  
Vol 8 (2) ◽  
pp. 264-278 ◽  
Author(s):  
Saturnino Maldonado-Bascon ◽  
Sergio Lafuente-Arroyo ◽  
Pedro Gil-Jimenez ◽  
Hilario Gomez-Moreno ◽  
Francisco Lopez-Ferreras
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Road Sign ◽  
Vector Machines ◽  
Sign Detection ◽  
Detection And Recognition

Traffic Sign Detection and Pattern Recognition Based on Binary Tree Support Vector Machines

2011 ◽  
Vol 204-210 ◽  
pp. 1394-1398
Author(s):  
Fa Ming Gong ◽  
Hai Juan Li
Keyword(s):  
Binary Tree ◽  
Feature Vector ◽  
Recognition System ◽  
Support Vector ◽  
Shape Classification ◽  
Traffic Sign ◽  
Road Sign ◽  
Vector Machines ◽  
Sign Detection ◽  
Traffic Sign Detection

This paper presents an automatic road sign detection and recognition system based on binary tree SVM. Color based segmentation techniques are employed for traffic sign detection. The coordinates position of traffic sign in images used for shape classification are obtained by orthogonal projection. An algorithm based on Hough transform was proposed to achieve better shape classification performance.Recognition of traffic signs are implemented using binary tree multi- classifer SVM with geometry semantic feature as the feature vector

scholarly journals Traffic Sign Detection and Recognition

2020 ◽  
Vol 5 (7) ◽  
pp. 1143-1146
Author(s):  
Anju C P ◽  
Andria Joy ◽  
Haritha Ashok ◽  
Joseph Ronald Pious ◽  
Livya George
Keyword(s):  
Language Processing ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Textual Data ◽  
Sign Detection ◽  
Local Residents ◽  
Non Local ◽  
Traffic Sign Detection ◽  
Detection And Recognition

As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The proposed system not only tackles the above-mentioned problem, but also to an extent ensuressafer driving by reducing accidents through conveying the traffic signs properly. The techniques usedto implement the system include digital image processing, natural language processing and machinelearning concepts. The implementation of the system includesthree major steps which are detection of traffic sign from a captured traffic scene, classification of traffic signs and finally conversion of classified traffic signs to audio message.

scholarly journals Traffic Sign Detection and Recognition using Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 1059-1063
Author(s):  
Mr. Mohammad Shabbir Sheikh
Keyword(s):  
Gabor Filter ◽  
Learning Technology ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Detection And Identification ◽  
Sign Detection ◽  
On The Road ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

scholarly journals Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3192 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Ting Rui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Gabor Wavelets ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Driving ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition ◽  
Grayscale Images

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

scholarly journals Automatic Traffic Sign Detection Practices: A Review

2020 ◽  
Vol 6 (12) ◽  
pp. 117-124
Author(s):  
Tania Joseph
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safe Navigation ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).

scholarly journals AUTOMATIC TRAFFIC SIGN DETECTION AND RECOGNITION USING MOBILE LIDAR DATA WITH DIGITAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 599-603
Author(s):  
H. Guan ◽  
Y. Yu ◽  
D. Li ◽  
J. Li
Keyword(s):  
High Performance ◽  
Digital Images ◽  
Point Clouds ◽  
Lidar Data ◽  
Recognition Method ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract. This paper presents a traffic sign detection and recognition method from mobile LiDAR data and digital images for intelligent transportation-related applications. The traffic sign detection and recognition method includes two steps: traffic sign interest regions are first extracted from mobile LiDRA data. Next, traffic signs are identified from digital images simultaneously collected from the multi-sensor mobile LiDAR systems via a convolutional capsule network model. The experimental results demonstrate that the proposed method obtains a promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images.

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