Real-time recognition of road traffic signs in video scenes

• Published in: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) pp. 125 - 130
• Main Authors: Farhat, Wajdi, Faiedh, Hassene, Souani, Chokri, Besbes, Kamel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2016
• Subjects: ADAS; Classification algorithms; Feature extraction; HSV; Image color analysis; MSER; Real-time systems; Road Traffic Sign Detection; Roads; Robustness; Shape; Template Matching; TSR
• DOI: 10.1109/ATSIP.2016.7523081

This paper presents a new algorithm for the detection and classification of real-time road traffic signs in the video. Our system is able to detect and classify triangular, circular, and octagonal signs of red and blue colors. The proposed system operates into two processing steps: (1) detection and (2) classification road signs. The system has detected candidate regions as Maximally Stable Extremal Regions (MSERs) in HSV color space with available robustness to variations in lighting conditions, especially red and blue. The detected candidate regions were then classified as method template matching, such as circular, octagonal, and triangular shapes. The proposed system is operating under a range of weather conditions and recognizes all classes of road traffic sign database. Results show a high success rate. In fact, the system maintains high performance for detection and classification steps whose F-measure are set to 0.95 and 0.92 respectively. We conclude, from these results, that the proposed system is invariant to rotation, translation, scale, even to partial occlusions. Moreover, results prove that the system is suitable and reliable for real-time processing.