Using self-organising maps in the detection and recognition of road signs

Prieto, Miguel S.; Allen, Alastair Robert

doi:10.1016/j.imavis.2008.07.006

Cited by 82 publications

(38 citation statements)

References 26 publications

(44 reference statements)

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“…To achieve a reliable detection performance under different lighting situations, traffic sign detection works have been conducted based on Y'CBCR [16], HSV [17], and CIECAM97 [18] color models. Except for global features, local invariant features, such as points of interest/regions [19], MSER [20], and Hough-like feature [21], have also been used in traffic sign detection.…”

Section: A Studies On Traffic Sign Detection and Recognitionmentioning

confidence: 99%

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Wen

Luo

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper presents a spatial-related traffic sign inspection process for sign type, position, and placement using mobile laser scanning (MLS) data acquired by a RIEGL VMX-450 system and presents its potential for traffic sign inventory applications. First, the paper describes an algorithm for traffic sign detection in complicated road scenes based on the retroreflectivity properties of traffic signs in MLS point clouds. Then, a point cloud-to-image registration process is proposed to project the traffic sign point clouds onto a 2-D image plane. Third, based on the extracted traffic sign points, we propose a traffic sign position and placement inspection process by creating geospatial relations between the traffic signs and road environment. For further inventory applications, we acquire several spatial-related inventory measurements. Finally, a traffic sign recognition process is conducted to assign sign type. With the acquired sign type, position, and placement data, a spatial-associated sign network is built. Experimental results indicate satisfactory performance of the proposed detection, recognition, position, and placement inspection algorithms. The experimental results also prove the potential of MLS data for automatic traffic sign inventory applications.

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Section: A Studies On Traffic Sign Detection and Recognitionmentioning

confidence: 99%

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Wen

Luo

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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“…A new method for pictogram classification was presented in [4] using Decisiontree-based support vector multi-class classifiers. Authors in [5] have used self-organizing maps for the detection and recognition of road signs. In the recognition stage, the nature of information is identified using the distribution of dark pixels in the pictogram of the detected sign.…”

Section: Related Workmentioning

confidence: 99%

A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

Rouabeh¹,

Abdelmoula²,

Masmoudi³

2016

ijacsa

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Abstract-This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

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“…There has been a large amount of research in the general problem of detecting traffic signs (stop signs, speed limits, etc) [Fang et al, 2004], [Fleyeh, 2005], [Prieto and Allen, 2009]. Our work largely avoids the need to detect traffic signs through the use of an annotated prior map.…”

Section: Related Workmentioning

confidence: 99%

Traffic light mapping and detection

Fairfield

Urmson

2011

2011 IEEE International Conference on Robotics and Automation

177

101

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Abstract-The outdoor perception problem is a major challenge for driver-assistance and autonomous vehicle systems. While these systems can often employ active sensors such as sonar, radar, and lidar to perceive their surroundings, the state of standard traffic lights can only be perceived visually. By using a prior map, a perception system can anticipate and predict the locations of traffic lights and improve detection of the light state. The prior map also encodes the control semantics of the individual lights. This paper presents methods for automatically mapping the three dimensional positions of traffic lights and robustly detecting traffic light state onboard cars with cameras. We have used these methods to map more than four thousand traffic lights, and to perform onboard traffic light detection for thousands of drives through intersections.

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Using self-organising maps in the detection and recognition of road signs

Cited by 82 publications

References 26 publications

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

Traffic light mapping and detection

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