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11th International Design and Test Symposium (IDT)

作者： Hamdi, Sabrine Faiedh, Hassene Souani, Chokri Besbes, Kamel Univ Monastir Microelect & Instrumentat Lab Monastir Tunisia Univ Sousse Natl Engn Sch Sousse Sousse Tunisia Univ Sousse Higher Inst Appl Sci & Technol Sousse Sousse Tunisia CRMN Sousse Tunisia

ISBN: (纸本)9781509049004

Automatic road signs recognition (RSR) aims to increase the safety for all traffic participants such as drivers and pedestrians. Despite all the significant advances in road sign detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions, namely daytime and nighttime. An automatic system equipped with a camera on the dashboard of the vehicle, must detect and alarms the driver when a road sign is present in poor lighting conditions. Most of existing RSR systems divided the problem into three modules: object detection, shape recognition and content classification. This paper's main objective is to develop an adequate and robust system for road signs detection independent of lighting. The road sign detection is based on the RGB-color space segmentation with an empirically determined threshold. It extracts the relevant red and blue regions in the image with limit values of Bounding Boxes. The extraction algorithm proposed and its performances are tested and discussed in a dataset of real driving scenarios, captured under various weather conditions.

关键词： road sign detection RGB-color space color segmentation Bounding Boxes various weather conditions

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A lighting independent vision based system for driver assistance

A lighting independent vision based system for driver assist...

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International Design and Test Workshop

作者： Sabrine Hamdi Hassene Faiedh Chokn Souani Kamel Besbes National Engineering School of Sousse University of Sousse Tunisia Microelectronics & Instrumentation Laboratory University of Monastir Tunisia Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Tunisia CRMN Centre for Research on Microelectronics and Nanotechnology Sousse Tunisia

ISBN: (纸本)9781509049011

关键词： various weather conditions road sign detection RGB-color space color segmentation Bounding Boxes

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detection and Recognition of Traffic signs from road Scene Images 12

Detection and Recognition of Traffic Signs from Road Scene I...

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12th International Conference on Frontiers of Information Technology (FIT)

作者： Malik, Zumra Siddiqi, Imran Bahria Univ Islamabad Pakistan

ISBN: (纸本)9781479975051

Automatic detection and recognition of road signs is an important component of automated driver assistance systems contributing to the safety of the drivers, pedestrians and vehicles. Despite significant research, the problem of detecting and recognizing road signs still remains challenging due to varying lighting conditions, complex backgrounds and different viewing angles. We present an effective and efficient method for detection and recognition of traffic signs from images. detection is carried out by performing color based segmentation followed by application of Hough transform to find circles, triangles or rectangles. Recognition is carried out using three state-of-the-art feature matching techniques, SIFT, SURF and BRISK. The proposed system evaluated on a custom developed dataset reported promising detection and recognition results. A comparative analysis of the three descriptors reveal that while SIFT achieves the best recognition rates, BRISK is the most efficient of the three descriptors in terms of computation time.

关键词： road sign detection road sign recognition Hough transform SIFT SURF BRISK

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Pattern recognition applied to infrared images for early alerts in fog 5

Pattern recognition applied to infrared images for early ale...

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5th Remote Sensing System Engineering Conference held as part of the SPIE Optics + Photonics and Optical Engineering + Applications Annual Meeting

作者： Boucher, Vincent. Marchetti, Mario Dumoulin, Jean Cord, Aurelien Cerema DTer Ouest 23 Ave Amiral ChauvinBP 69 F-49136 Les Ponts De Ce France Cerema DTer Est F-54510 Tomblaine France IFSTTAR CoSys F-44344 Bouguenais France IFSTTAR Paris F-77447 Paris 2 France INRIA IRISA I4S Team F-35042 Rennes France

ISBN: (纸本)9781628412505

Fog conditions are the cause of severe car accidents in western countries because of the poor induced visibility. Its forecast and intensity are still very difficult to predict by weather services. Infrared cameras allow to detect and to identify objects in fog while visibility is too low for eye detection. Over the past years, the implementation of cost effective infrared cameras on some vehicles has enabled such detection. On the other hand pattern recognition algorithms based on Canny filters and Hough transformation are a common tool applied to images. Based on these facts, a joint research program between IFSTTAR and Cerema has been developed to study the benefit of infrared images obtained in a fog tunnel during its natural dissipation. Pattern recognition algorithms have been applied, specifically on road signs which shape is usually associated to a specific meaning (circular for a speed limit, triangle for an alert,.). It has been shown that road signs were detected early enough in images, with respect to images in the visible spectrum, to trigger useful alerts for Advanced Driver Assistance Systems.

关键词： infrared road sign detection adverse weather conditions

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Real-time Automatic road sign detection

Real-time Automatic Road Sign Detection

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5th International Conference on Image and Graphics

作者： Wu, Jianping Si, Maoxin Tan, Fangyong Gu, Caidong Suzhou Vocat Univ JiangSu Prov Support Software Engn R&D Ctr Modern Suzhou 215104 Peoples R China

ISBN: (纸本)9780769538839

This paper presents a real-time robust algorithm to automatically detect and classify round road signs on Chinese highways. As most of road signs on Chinese highways have circular borders, the detection system first takes advantage of a real-time circle detection algorithm to extract the regions of interest (ROI). Then the color and texture pattern in each ROI is analyzed and compared with sample signs to both validate the candidate road sign and identify their types. The algorithm is implemented in Visual C++ and tested on a laptop powered by an Intel Centrino Duo CPU at 1.8GHz. The experiment shows the algorithm's speed is fast, 83 ms/images on average for image size of 640X480, and its false negative (failure to detect) is 21 signs or 4% (failure to detect) for a pool of 530 different road signs in a pool of 1000 pictures. And there are 3 non-road-sign objects incorrectly recognized as valid road signs.

关键词： road sign detection road sign recognition digital image processing computer vision

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Automatic road Inventory Using LiDAR

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Procedia Economics and Finance 2014年 12卷 363-370页

作者： Jaromir Landa David Prochazka Mendel University in Brno Faculty of Business and Economics Zemědělská 1 Brno 61300 Czech Republic

road inventory is very important for effective transportation management. This process is usually done by a technician in the field, manually from obtained aerial/satellite images or semi-automatically by a software application. The first two options are very time consuming, hence expensive. Many companies therefore try to process at least part of this task automatically. The road inventory process comprises an identification of objects that can be found either on the road or in the road proximity. Examples include road signs, road markings, guardrails and many more. We can register much information about these objects, such as their position, condition or type. Generally, there are two sources for the extraction of information needed for the inventory. The first source is a set of images captured by a camera. The second source is data captured by a LiDAR. Either of them can provide different information; therefore, the choice of the source must be made with regard to the required information. In our article, we compare information that can be obtained from camera images and LiDAR measurements. This comparison is presented on three example objects: traffic signs, road markings and general pole-shaped objects (e.g. city lights or trees). Further, we describe a process based on our algorithm that detects traffic signs in LiDAR measurement and transforms the results to a common format used in geographic information systems. We test our method on an approximately two-kilometer long road in an urban area.

关键词： LiDAR Point Cloud road sign detection

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road sign detection using eigen colour

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IET COMPUTER VISION 2008年第3期2卷 164-177页

作者： Tsai, L. -W. Hsieh, J. -W. Chuang, C. -H. Tseng, Y. -J. Fan, K. -C. Lee, C. -C. Natl Cent Univ Inst Comp Sci & Informat Engn Jhong Rd Jhongli 320 Taiwan Yuan Ze Univ Dept Elect Engn Jhongli 320 Taiwan Fo Guang Univ Jiaosi 26247 Yilan County Taiwan

A novel colour-based method to detect road signs directly from videos is presented. A road sign is usually painted with different colours to show its functionalities. To detect it, different detectors should be designed to deal with its colour changes. A statistic linear model of colour change space that makes road sign colours be more compact and thus sufficiently concentrated on a smaller area is presented. On this model, only one detector is needed to detect different road signs even though their colours are different. The model is global and can be used to detect any new road signs. The colour model is invariant to different perspective effects and occlusions. After that, a radial basis function (RBF) network is then used to train a classifier to find all possible road sign candidates from road scenes. Furthermore, a veri. cation process is applied to verify each candidate using its contour feature. After veri. cation, a recti. cation process is used for rectifying each skewed road sign so that its embedded texts can be well segmented and recognised. Due to the filtering effect of the proposed colour model, different road signs can be very efficiently and effectively detected from videos. Experimental results have proved that the proposed method is robust, accurate and powerful in road sign detection.

关键词： embedded text video radial basis function network image colour analysis radial basis function networks road scene classification eigen colour image classification statistical analysis filtering effect feature extraction rectification process statistic linear model driver information systems video signal processing contour feature intelligent driver support system road sign detection filtering theory road sign recognition image segmentation road sign verification process eigenvalues and eigenfunctions

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EVALUATION OF A road sign PRE-detection SYSTEM BY IMAGE ANALYSIS

EVALUATION OF A ROAD SIGN PRE-DETECTION SYSTEM BY IMAGE ANAL...

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4th International Conference on Computer Vision Theory and Applications

作者： Foucher, Philippe Charbonnier, Pierre Kebbous, Houssem Lab Ponts & Chaussees ERA 27 LCPC F-67035 Strasbourg France

ISBN: (纸本)9789898111692

In this paper, we introduce a pre-detection algorithm dedicated to French danger-warning and prohibitory road signs. The proposed method combines color, shape, location and symmetry features to select among large image databases, a small subset of pictures that probably contain road signs. We report the results of a systematic experimental assessment that we performed on five image databases, comprised of more than 26,000 images, covering 176 km and containing 371 traffic signs, among which a non-negligible amount (about 5% in average) is damaged. The experiments show that about 10% images of the sequences are selected and more than 87% traffic signs are detected. The missed objects always correspond to dirty, worn-out or badly oriented signs that would be difficult to detect even for a human operator.

关键词： Image analysis road sign detection road sign inventory Color Shape Symmetry Evaluation

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GPU implementation of a road sign detector based on particle swarm optimization

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EVOLUTIONARY INTELLIGENCE 2010年第3-4期3卷 155-169页

作者： Mussi, Luca Cagnoni, Stefano Cardarelli, Elena Daolio, Fabio Medici, Paolo Porta, Pier Paolo Univ Parma Dipartimento Ingn Informaz Viale G Usberti 181a I-43124 Parma Italy Univ Lausanne Informat Syst Inst ISI HEC CH-1015 Lausanne Switzerland

road sign detection is a major goal of the Advanced Driving Assistance Systems. Most published work on this problem share the same approach by which signs are first detected and then classified in video sequences, even if different techniques are used. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel modular and scalable approach to road sign detection based on Particle Swarm Optimization, which takes into account both shape and color to detect signs. In our approach, in particular, the optimization of a single fitness function allows both to detect a sign belonging to a certain category and, at the same time, to estimate its position with respect to the camera reference frame. To speed up processing, the algorithm implementation exploits the parallel computing capabilities offered by modern graphics cards and, in particular, by the Compute Unified Device Architecture by nVIDIA. The effectiveness of the approach has been assessed on both synthetic and real video sequences, which have been successfully processed at, or close to, full frame rate.

关键词： Particle swarm optimization road sign detection GPU computing Parallel computing

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ESARR: Enhanced Situational Awareness via road sign Recognition

ESARR: Enhanced Situational Awareness via Road Sign Recognit...

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Conference on Unmanned Systems Technology XII

作者： Perlin, V. E. Johnson, D. B. Rohde, M. M. Lupa, R. M. Fiorani, G. Mohammad, S. Quantum Signal LLC 3741 Plaza Dr Ann Arbor MI 48108 USA US Army Tank Automot Res Dev & Engn Ctr Warren MI 48397 USA

ISBN: (纸本)9780819481566

The enhanced situational awareness via road sign recognition (ESARR) system provides vehicle position estimates in the absence of GPS signal via automated processing of roadway fiducials (primarily directional road signs). sign images are detected and extracted from vehicle-mounted camera system, and preprocessed and read via a custom optical character recognition (OCR) system specifically designed to cope with low quality input imagery. Vehicle motion and 3D scene geometry estimation enables efficient and robust sign detection with low false alarm rates. Multi-level text processing coupled with GIS database validation enables effective interpretation even of extremely low resolution low contrast sign images. In this paper, ESARR development progress will be reported on, including the design and architecture, image processing framework, localization methodologies, and results to date. Highlights of the real-time vehicle-based directional road-sign detection and interpretation system will be described along with the challenges and progress in overcoming them.

关键词： Traffic sign Recognition Situational Awareness road sign detection OCR localization GIS

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