roadsign recognition is a part of driver support systems. Its main aim is the increase of traffic safety by calling the driver's attention to the presence of key traffic signs. Additionally, a vision-based system...
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roadsign recognition is a part of driver support systems. Its main aim is the increase of traffic safety by calling the driver's attention to the presence of key traffic signs. Additionally, a vision-based system able to detect and classify traffic signs from road images in real-time would also be useful as a support tool for guidance and navigation of intelligent vehicles. This paper proposes a new method for the detection and recognition of traffic signs using self-organising maps (SOM). This method first detects potential roadsigns by analysing the distribution of red pixels within the image, and then identifies these roadsigns from the distribution of dark pixels in their pictograms. Additionally, a novel hybrid system combining programmable hardware and artificial neural networks for embedded machine vision is introduced, and a prototype of this system is used in the implementation of the application. The experiments indicate a good performance of the new approach using SOM in both speed and classification accuracy. (C) 2008 Elsevier B.V. All rights reserved.
In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the roadsigns recogniti...
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In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the roadsigns recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.
A fast and robust method to detect and recognize scaled and skewed roadsigns is proposed in this paper. In the detection stage, the input color image is first quantized in HSV color model. Border tracing those region...
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A fast and robust method to detect and recognize scaled and skewed roadsigns is proposed in this paper. In the detection stage, the input color image is first quantized in HSV color model. Border tracing those regions with the same colors as roadsigns is adopted to find the regions of interest (ROI). The ROls are then automatically adjusted to fit roadsign shape models so as to facilitate detection verification even for scaled and skewed roadsigns in complicated scenes. Moreover, the ROI adjustment and verification are both performed only on border pixels;thus, the proposed roadsign detector is fast. In the recognition stage, the detected roadsign is normalized first. Histogram matching based on polar mesh is then adopted to measure the similarity between the scene and model roadsigns to accomplish recognition. Since histogram matching is fast and has high tolerance to distortion and deformation while contextual information can still be incorporated into it in a natural and elegant way, our method has high recognition accuracy and fast execution speed. Experiment results show that the detection rate and recognition accuracy of our method can achieve 94.2% and 91.7%, respectively. On an average, it takes only 4-50 and 10 ms for detection and recognition, respectively. Thus, the proposed method is effective, yet efficient. (c) 2007 Wiley Periodicals, Inc.
In this thesis, video frames acquired by a camera in a moving car are processed for detection of candidates of triangular, rectangular and circular traffic/roadsigns based on mainly shape information by performing co...
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In this thesis, video frames acquired by a camera in a moving car are processed for detection of candidates of triangular, rectangular and circular traffic/roadsigns based on mainly shape information by performing contour analysis. Color information is utilized as an auxiliary method to improve detection. Then recognition based on template matching is realized on detected traffic/roadsign candidates. detection and recognition results of traffic/roadsigns in video frames taken in different time intervals of day for these methods are compared. After implementation, results show that the video scene taken in a sunny day in the afternoon gives better results than others. Binary threshold plays a great role in detection with respect to Canny edge detector especially for triangular and rectangular traffic signs. Higher number of binary threshold levels improves detection in general. In addition, the recognition rate for triangular and rectangular traffic/roadsigns is higher than that of circular sings in general by the methods used in this study.
A fast and robust framework for incrementally detecting text on roadsigns from video is presented in this paper. This new framework makes two main contributions. 1) The framework applies a divide-and-conquer strategy...
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A fast and robust framework for incrementally detecting text on roadsigns from video is presented in this paper. This new framework makes two main contributions. 1) The framework applies a divide-and-conquer strategy to decompose the original task into two subtasks, that is, the localization of roadsigns and the detection of text on the signs. The algorithms for the two subtasks are naturally incorporated into a unified framework through a feature-based tracking algorithm. 2) The framework provides a novel way to detect text from video by integrating two-dimensional (2-D) image features in each video frame (e.g., color, edges, texture) with the three-dimensional (3-D) geometric structure information of objects extracted from video sequence (such as the vertical plane property of roadsigns). The feasibility of the proposed framework has been evaluated using 22 video sequences captured from a moving vehicle. This new framework gives an overall text detection rate of 88.9% and a false hit rate of 9.2 %. It can easily be applied to other tasks of text detection from video and potentially be embedded in a driver assistance system.
This paper addresses the design of a system for automatic sign interpretation (ASI) with specific applications to roadsign imagery for driver assistance. Other applications include intelligent highway systems, and si...
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ISBN:
(纸本)0780394577
This paper addresses the design of a system for automatic sign interpretation (ASI) with specific applications to roadsign imagery for driver assistance. Other applications include intelligent highway systems, and sign inventory systems for transportation departments. This paper extends past work in which a prototype system for the detection of roadsigns was designed and tested on a limited set of images and sign types [1]. The specific enhancements reported in this paper include the following: (i) enhanced dual-band spectral analysis in the hue-saturation-intensity (HSI) and RGB domains, (ii) the introduction of a relational analysis scheme to form the identification stage, (iii) the creation of a larger database of roadsign images encompassing all roadsign types encountered in the United States, and (iv) testing under adverse conditions with non-ideal roadsign images. The proposed method can be adapted for Implementation in real time as this was one of the design considerations. The human cognition of signs under adverse conditions is one of the basic research questions that is yet to be answered. The ASI system designed will help to shed some fight on the human cognitive process.
Design of an on-board processor that enables recognition of a given road sip affected by different distortions is presented. The road sip recognition system is based on a nonlinear processor. Analysis of different fil...
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ISBN:
(纸本)0819441856
Design of an on-board processor that enables recognition of a given road sip affected by different distortions is presented. The road sip recognition system is based on a nonlinear processor. Analysis of different filtering methods allows us to select the best techniques to overcome a variety of distortions. The proposed recognition system has been tested in real still images as well as in video sequences. Scenes were captured in real environments, with cluttered backgrounds and contain many distortions simultaneously. Recognition results for various images show that the processor is able to properly detect a given roadsign even if it is varying in scale, slightly tilted or viewed under different angles. Recognition is also achieved when dealing with partially occluded roadsigns. In addition, the system is robust to illumination fluctuations.
Tato bakalářská práce se zabývá metodami detekce vybraných objektů ve videu a importováním těchto objektů do centrální databáze OpenStreetMap na základě je...
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Tato bakalářská práce se zabývá metodami detekce vybraných objektů ve videu a importováním těchto objektů do centrální databáze OpenStreetMap na základě jejich geografické poloze. Zaměřena je z velké části na rozpoznávání dopravních značek. První část stručně popisuje některé nejpoužívanější metody a samotný projekt OpenStreetMap. V nasledujících kapitolách je uveden podrobnější přehled použitých metod navrhnutého systému, jeho implementace a testování. Závěr obsahuje zhodnocení celé práce a jsou zde uvedené možné rozšíření.
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