Road traffic sign detection and classification

Escalera, Arturo de la; Moreno, Luís; Salichs, Miguel Á.; Armingol, José María

doi:10.1109/41.649946

Cited by 473 publications

(210 citation statements)

References 18 publications

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“…The classification is then performed by a neural network. The detected image region is used as network input and image pixels are taken directly as features (Escalera et al, 1997;Franke et al, 1998). There are some issues specific to the recognition of road signs :…”

Section: Road Sign Recognitionmentioning

confidence: 99%

Road sign classification using Laplace kernel classifier

Pacĺık

Novovičová

Pudil

et al. 2000

Pattern Recognition Letters

133

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Driver support systems of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method. To maximize the pseudo-likelihood function, an ExpectationMaximization algorithm is used. The algorithm has been tested on a dataset with more than 4 900 noisy images. A comparison to other classification methods is also given.Key words: Road sign recognition, Kernel density estimation, Expectation-maximization algorithm Road Sign RecognitionIn an intelligent vehicle a Driver Support System (DSS) should work as a driver copilot, continuously monitoring the driver, vehicle and the environment in order to facilitate human decisions about immediate vehicle guidance and navigation (Nagel). To be able to help the driver with decision making, the DSS must understand the current traffic situation. Therefore, it should create and maintain a model of its neighborhood. Because of the dominant role of visual information for the corresponding author : pavel@ph.tn.tudelft.nl 1 The final work on the paper has been done in the Pattern Recognition Group, Delft Univ. of Technology, P.O. Box 5046, 2600 GA, Delft, The Netherlands human driver, computer vision methods are often used in intelligent vehicle prototypes for the creation of such model. Road signs offer, among the other traffic devices, a lot of important information about the current traffic situation. Two basic road sign groups exist -ideogram-based and text-based signs. While the first group uses simple ideographs to express the sign meaning, the second one contains road signs with texts, arrows and other symbols. This article is concerned with the recognition of ideogram-based road signs using statistical pattern recognition methods. A comprehensive study of road sign recognition presented by Lalonde and Li (1995) compiles information about related algorithms, research groups and results. Several research projects dealing with the road

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Section: Road Sign Recognitionmentioning

confidence: 99%

Road sign classification using Laplace kernel classifier

Pacĺık

Novovičová

Pudil

et al. 2000

Pattern Recognition Letters

133

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“…Several works have recently focused on traffic sign detection and recognition [1], [2], [3], [4], [5] and [6]. Specifically, in [7] it is described the framework that we use in this research.…”

Section: Introductionmentioning

confidence: 99%

Road Sign Analysis Using Multisensory Data

López-Sastre

Lafuente-Arroyo

Gil-Jiménez

et al.

Nature Inspired Problem-Solving Methods in Knowledge Engineering

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Abstract. This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device, all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters. Results show that the described calculation methodology offers a correct estimation for all types of traffic signs.

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“…Many algorithms have been proposed to solve this problem. These methods can be divided into shapebased [5,8,11,16] and color-based segmentation [3,4,13].…”

Section: Introductionmentioning

confidence: 99%

Color-Based Road Sign Detection and Tracking

Lopez

Fuentes

Lecture Notes in Computer Science

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Abstract. This paper describes a general framework for the detection and tracking of traffic and road signs from image sequences using only color information. The approach consists of two independent parts. In the first we use a set of Gaussian distributions that model each color for detecting road and traffic signs. In the second part we track the targets detected in the first step over time. Our approach is tested using image sequences with high clutter that contain targets with the presence of rotation and partial occlusion. Experimental results show that the proposed system detects on average 97% of the targets in the scene in near real-time with an average of 2 false detections per sequence.

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Road traffic sign detection and classification

Cited by 473 publications

References 18 publications

Road sign classification using Laplace kernel classifier

Road sign classification using Laplace kernel classifier

Road Sign Analysis Using Multisensory Data

Color-Based Road Sign Detection and Tracking

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