In-vehicle camera traffic sign detection and recognition

• Published in: Machine vision and applications Vol. 22; no. 2; pp. 359 - 375
• Main Authors: Ruta, Andrzej, Porikli, Fatih, Watanabe, Shintaro, Li, Yongmin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.03.2011; Springer Nature B.V
• Subjects: Algorithms; Clustering; Communications Engineering; Computer Science; Detectors; Face recognition; Image classification; Image Processing and Computer Vision; Machine learning; Networks; Object recognition; Original Paper; Pattern Recognition; Recognition; Regression; Regression analysis; Representations; Tracking; Traffic control; Traffic signs; Vision systems; Confidence-weighted mean shift; SimBoost; Traffic sign recognition; Regression tracking
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-009-0231-x

In this paper, we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework, a generic detector refinement procedure based on mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response’s confidence can be sensibly estimated. The track of an already established candidate is maintained over time using an instance-specific tracking function that encodes the relationship between a unique feature representation of the target object and the affine distortions it is subject to. We show that this function can be learned on-the-fly via regression from random transformations applied to the image of the object in known pose. Secondly, we demonstrate its capability of reconstructing the full-face view of a sign from substantial view angles. In the recognition stage, a concept of class similarity measure learned from image pairs is discussed and its realization using SimBoost , a novel version of AdaBoost algorithm, is analyzed. Suitability of the proposed method for solving multi-class traffic sign classification problems is shown experimentally for different feature representations of an image. Overall performance of our system is evaluated based on a prototype C++ implementation. Illustrative output generated by this demo application is provided as a supplementary material attached to this paper.