General traffic sign recognition by feature matching

Ren, Feixiang; Huang, Jinsheng; Jiang, Ruobing; Klette, Reinhard

doi:10.1109/ivcnz.2009.5378370

Cited by 46 publications

(25 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, sign detection methods mostly rely on the threshold segmentation based on different color spaces, or feature point extraction and matching algorithms like SIFT or SURF [24] . On this basis, the signs are classified and identified by machine learning methods like random forests [25] or SVM (support vector machine) [26] .…”

Section: Road Sign Detectionmentioning

confidence: 99%

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Yuan

Chen

Zhao³

et al. 2017

J. Comput. Sci. Technol.

View full text Add to dashboard Cite

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

show abstract

Section: Road Sign Detectionmentioning

confidence: 99%

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Yuan

Chen

Zhao³

et al. 2017

J. Comput. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Since the algorithms have to be trained based on many images appearing in different scaling, orientation and illumination contexts, they are usually just implemented for a few types such as speed signs (Ren et al 2009). Another method for the classification process is template matching.…”

Section: Classification Of Road Signsmentioning

confidence: 99%

“…This intensity based image correlation approach is, for example, used by Piccioli et al (1996) and Malik et al (2007). In its basic form, it is not robust regarding scaling, rotation or affine transformations in general and is sensitive to illumination changes (Ren et al 2009). …”

Section: Classification Of Road Signsmentioning

confidence: 99%

“…If the SIFT approach by Lowe (2004) is used, the extracted features are invariant in terms of translation, rotation and scaling as well as insensitive to illumination changes, image noise and small geometric deformations (Reiterer et al 2009, Ren et al 2009). Maldonado-Bascón et al (2007) implemented two types of SVM which enable their algorithms to handle translations, rotations, scaling and mostly partial occlusions.…”

Section: Further Approaches For the Detection And Classification Of Rmentioning

confidence: 99%

See 1 more Smart Citation

Automated 3d Road Sign Mapping With Stereovision-Based Mobile Mapping Exploiting Disparity Information From Dense Stereo Matching

Cavegn

Nebiker

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper presents algorithms and investigations on the automated detection, classification and mapping of road signs which systematically exploit depth information from stereo images. This approach was chosen due to recent progress in the development of stereo matching algorithms enabling the generation of accurate and dense depth maps. In comparison to mono imagery-based approaches, depth maps also allow 3D mapping of the objects. This is essential for efficient inventory and for future change detection purposes. Test measurements with the mobile mapping system by the Institute of Geomatics Engineering of the FHNW University of Applied Sciences and Arts Northwestern Switzerland demonstrated that the developed algorithms for the automated 3D road sign mapping perform well, even under difficult to poor lighting conditions. Approximately 90% of the relevant road signs with predominantly red, blue and yellow colors in Switzerland can be detected, and 85% can be classified correctly. Furthermore, fully automated mapping with a 3D accuracy of better than 10 cm is possible.

show abstract

“…In addition to detecting strong colors, Gómez-Moreno et al [6] proposed a chromatic and achromatic decomposition method to find the image pixels lacking color information based on the idea of closeness of the R, G, and B components. Rather than directly using RGB, other studies used different color spaces that are more immune to lighting changes rather than directly using RGB, such as the HSI color space [7]- [9], the HSV color space [10], the LUV color space [11], the YUV color space [12], and the Lab color space [13]. The most common among these color spaces is the HSI space, which focuses on the hue and saturation components to prevent lighting dependence relations and sometimes includes intensity information to reduce hue and saturation instabilities.…”

Section: Introductionmentioning

confidence: 99%

Rapid Multiclass Traffic Sign Detection in High-Resolution Images

Liu

Chang

Chen

2014

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

This paper describes a traffic sign detection (TSD) framework that is capable of rapidly detecting multiclass traffic signs in high-resolution images while achieving a high detection rate. There are three key contributions. The first is the introduction of two features called multiblock normalization local binary pattern (MN-LBP) and tilted MN-LBP (TMN-LBP), which are able to express multiclass traffic signs effectively. The second is a tree structure called split-flow cascade, which utilizes common features of multiclass traffic signs to construct a coarse-to-fine TSD detector. The third contribution is the Common-Finder AdaBoost (CF.AdaBoost) algorithm, which is designed to find common fea- tures of different training sets to develop an efficient Split-Flow Cascade tree (SFC-tree) for multiclass TSD. Through experiments with an evaluation data set of high-resolution images, we show that the proposed framework is able to detect multiclass traffic signs with high detection accuracy in real time and that it outperforms the state-of-the-art approaches at detecting a large number of different types of traffic signs rapidly without using any color information.Index Terms-Common-Finder AdaBoost (CF.AdaBoost), multiblock normalization local binary pattern (MN-LBP), multiclass object detection, split-flow cascade, traffic sign detection (TSD).

show abstract

General traffic sign recognition by feature matching

Cited by 46 publications

References 11 publications

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Automated 3d Road Sign Mapping With Stereovision-Based Mobile Mapping Exploiting Disparity Information From Dense Stereo Matching

Rapid Multiclass Traffic Sign Detection in High-Resolution Images

Contact Info

Product

Resources

About