CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: CIRCULAR ROAD SIGN EXTRACTION FROM STREET LEVEL IMAGES USING COLOUR, SHAPE AND TEXTURE DATABASE MAPS A. Arlicot, B. Soheilian and N. Paparoditis

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 CIRCULAR ROAD SIGN EXTRACTION FROM STREET LEVEL IMAGES USING COLOUR, SHAPE AND TEXTURE DATABASE MAPS A. Arlicot *, B. Soheilian and N. Paparoditis Institut Géographique National, Laboratoire Matis, 73, avenue de Paris, 94165 Saint-Mandé cedex, France aurore.arlicot@polytech.univ-nantes.fr, bahman.soheilian@ign.fr, nicolas.paparoditis@ign.fr http ://recherche.ign.fr/labos/matis/ KEY WORDS: mobile mapping system, road sign recognition, color detection, ellipse detection, pattern matching ABSTRACT Detection and recognition of road signs can constitute useful tools in driving assistance and autonomous navigation systems. We aim at generating a road sign database that can be used for both georeferencing in autonomous vehicle navigation systems and also in high scale 3D city modelling. This paper proposes a robust algorithm that can detect road signs shape and recognizes their types. 1 INTRODUCTION Road signs are very important features for providing rules of navigation. Indeed, they are key landmarks when navi gating on the roads. Their visual properties are very strong because they have been designed to be remarkable and un- missible objects. Road signs are thus key objects to en rich road model databases to generate roadbooks, short est paths, etc. The automatic detection and recognition of road signs from images (together with objects such as road marks) is thus a key topic and issue for road model updat ing but also for tomorrow's applications of these databases, i.e. driving assistance, and accurate localisation functions for autonomous navigation. Most of the previous work in image based road sign extraction deal with three following issues: • Color detection : road signs are often red or blue with some black and white. Many authors used this property to detect them. Often, color base rules are defined in a color space and used for segmentation, (de la Escalera, 1997) use RGB color space and work with relations between the red , green and blue. Other authors works with color spaces that are less sensitive to lighting changes. Although the HSI (Hue, Satu ration, Intensity) space is the most common (Piccioli et al., 1996). More complicated color space such as LCH (Lightness, Chroma, Hue) (Shaposhnikov et al., 2002) and CIELAB (Reina et al., 2006) are also used. • Shape detection: road signs forms are often rect angular, triangular or circular. In order to strengthen the detection, some authors propose to detect these geometric forms within ROIs * 1 provided by color de tection. (Ishizuka and Hirai, 2004) present an algo rithm for circular road sign detection. (Habib and Jha, 2007) propose an algorithm for road sign forms de tection by line fitting. An interesting measure of el- lipticity, rectangularity, and triangularity is proposed by (Rosin, 2003). • T^pe recognition: It consists in recognising road sign type using its pictorial information. It is often *A. Arlicot is currently at Polytech’Nantes, IRCCyN lab France. 1 Region of Interest performed by comparing the inside texture of a de tected road sign with the textures in a database. For this purpose different kind of algorithms are used in the state of the art. (Priese et al., 1995) propose an algorithm that is based on neural networks. SIFT de scriptors are used by (Aly and Alaa, 2004). (de la Es calera et al., 2004) used intensity correlation score as a measure of similarity to compare the detected road sign with a set of standard signs. 2 OUR STRATEGY We propose an algorithm consisting in three main steps. Diagram of Figure 1 shows the pipeline of our algorithm. First step uses color properties of signs and perform a pre detection (Section 3). It provides a set of ROIs in image space. Then, an ellipse detection algorithm is applied to detect circular shape signs within the ROIs (Section 4). The detected shapes are considered as road sign hypothe ses. Final step consists in validation or rejection of hy potheses. This is performed by matching detected hypothe ses with a set of standard circular signs of the same color (Section 5). Results and evaluations are presented in Sec tion 6. 3 COLOR DETECTION A large number of road signs are blue or red. It can sim plify their detection by looking for red and blue pixels. However their RGB values depend on illumination condi tions. We use HSV (Hue, Saturation, Value, see Equation 1) color space because it is robust against variable condi tions of luminosity. In order to choose the adapted thresh old of saturation and hue, we learn these parameters from a set of road sign sample in different illumination condi tions. Figure 2(a) shows our running example image and result of blue color detection is shown in Figure 2(b). In order to provide ROIs, connected pixels are labeled (see Figure 2(c)). Each label defines a window in image space. The following form detection and validation steps are per formed within these windows.

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