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Mean shift and log-polar transform for road sign detection

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Abstract

Road sign detection is an important function for driver assistance systems. Although it has been studied for many years, it still has some performance limitations. This paper proposes a new method for road sign detection by employing both color and shape cues. The proposed method consists of three steps. First, the initial image is pre-processed using mean shift clustering algorithm. The clustering is carried out based on color information. Second, a random forest classifier is used to segment the clustered image. In the final step, a shape based classification is performed using a log-polar transform and cross correlation technique. The proposed detection method has been tested on both the German traffic sign detection benchmark (GTSDB) and the Swedish traffic signs (STS) datasets, and yields to 93.50 % and 94.22 % on the GTSDB dataset in terms of F-measure and area under curve(AUC), respectively. These results are satisfactory when compared to recent state-of-the-art methods.

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Authors and Affiliations

  1. LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000, Agadir, Morocco

    Ayoub Ellahyani & Mohamed El Ansari

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  1. Ayoub Ellahyani
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  2. Mohamed El Ansari
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Correspondence to Ayoub Ellahyani.

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Ellahyani, A., Ansari, M.E. Mean shift and log-polar transform for road sign detection. Multimed Tools Appl 76, 24495–24513 (2017). https://doi.org/10.1007/s11042-016-4207-3

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  • DOI: https://doi.org/10.1007/s11042-016-4207-3

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