Complete Vision-Based Traffic Sign Recognition Supported by an I2V Communication System

This paper presents a complete traffic sign recognition system based on vision sensor onboard a moving vehicle which detects and recognizes up to one hundred of the most important road signs, including circular and triangular signs. A restricted Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM). A novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed. For that purpose infrastructure-to-vehicle (I2V) communication and a stereo vision sensor are used. Furthermore, the outputs provided by the vision sensor and the data supplied by the CAN Bus and a GPS sensor are combined to obtain the global position of the detected traffic signs, which is used to identify a traffic sign in the I2V communication. This paper presents plenty of tests in real driving conditions, both day and night, in which an average detection rate over 95% and an average recognition rate around 93% were obtained with an average runtime of 35 ms that allows real-time performance

Robust Traffic Sign Detection by means of Vision and V2I Communications

14th International IEEE Annual Conference on Intelligent Transportation Systems - ITSC, , 05/10/2011-07/10/2011, Washington DC, Estados UnidosThis paper presents a complete traffic sign recognition system, including the steps of detection, recognition and tracking. The Hough transform is used as detection method from the information extracted in contour images, while the proposed recognition system is based on Support Vector Machines (SVM), and is able to recognize up to one hundred of the main road signs. Besides a novel solution to the problem of discarding detected signs that do not pertain to the host road is proposed, for that purpose vehicle-to-infrastructure (V2I) communication and stereo information is used. This paper presents plenty of tests in real driving conditions, both day and night, in which a high success rate and low number of false negatives and true positives were obtained, and an average runtime of 35 ms, allowing real-time performance

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad TIN2013-46801-C4-1-

Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Made available in DSpace on 2018-11-29T04:54:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01Instituto Federal do Ceara (IFCE)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Programa Operacional Regional do Norte (NORTE2020) through Fundo Europeu de Desenvolvimento Regional (FEDER)The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 mu s to recognize a sign, while kNN took 11,721 ls and SVM 12,595 ls. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.Inst Fed Fed Educ Ciencia & Tecnol Ceara IFCE, Lab Proc Digital Imagens & Simulacao Computac, Juazeiro Do Norte, Ceara, BrazilUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilUniv Fortaleza, Programa Posgrad Informat Aplicada, Lab Bioinformat, Fortaleza, CE, BrazilUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Dept Engn Mecan, Oporto, PortugalUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilInstituto Federal do Ceara (IFCE): PROINFRA/2013Instituto Federal do Ceara (IFCE): PROAPP/2014Instituto Federal do Ceara (IFCE): PROINFRA/2015CNPq: 470501/2013-8CNPq: 301928/2014-2: NORTE-01-0145-FEDER-00002