Large-scale classification of traffic signs under real-world conditions

Lykele Hazelhoff; Ivo Creusen; Dennis van de Wouw; Peter H. N. de With

doi:10.1117/12.910490

9 February 2012 Large-scale classification of traffic signs under real-world conditions

Lykele Hazelhoff, Ivo Creusen, Dennis van de Wouw, Peter H. N. de With

Proceedings Volume 8304, Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI; 83040W (2012) https://doi.org/10.1117/12.910490
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Traffic sign inventories are important to governmental agencies as they facilitate evaluation of traffic sign locations and are beneficial for road and sign maintenance. These inventories can be created (semi-)automatically based on street-level panoramic images. In these images, object detection is employed to detect the signs in each image, followed by a classification stage to retrieve the specific sign type. Classification of traffic signs is a complicated matter, since sign types are very similar with only minor differences within the sign, a high number of different signs is involved and multiple distortions occur, including variations in capturing conditions, occlusions, viewpoints and sign deformations. Therefore, we propose a method for robust classification of traffic signs, based on the Bag of Words approach for generic object classification. We extend the approach with a flexible, modular codebook to model the specific features of each sign type independently, in order to emphasize at the inter-sign differences instead of the parts common for all sign types. Additionally, this allows us to model and label the present false detections. Furthermore, analysis of the classification output provides the unreliable results. This classification system has been extensively tested for three different sign classes, covering 60 different sign types in total. These three data sets contain the sign detection results on street-level panoramic images, extracted from a country-wide database. The introduction of the modular codebook shows a significant improvement for all three sets, where the system is able to classify about 98% of the reliable results correctly.

Citation Download Citation

Lykele Hazelhoff, Ivo Creusen, Dennis van de Wouw, and Peter H. N. de With "Large-scale classification of traffic signs under real-world conditions", Proc. SPIE 8304, Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI, 83040W (9 February 2012); https://doi.org/10.1117/12.910490

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