Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Circular road signs recognition with soft classifiers
Integrated Computer-Aided Engineering - Artificial Neural Networks
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Real-time detection of the triangular and rectangular shape road signs
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Committee machine for road-signs classification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Visual sign information extraction and identification by deformable models for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Building Road-Sign Classifiers Using a Trainable Similarity Measure
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
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Recognition of traffic signs by systems of the intelligent vehicles can increase safety and comfort of driving. It can be also used for highway inspection. In this paper we present architecture of such a system, with special focus on fast sign tracking method. In each frame an adaptive window is built around each area with high probability of existence of an object to be tracked. However, contrary to the mean-shift algorithm, it is not necessary to compute a centroid for each such object. Thus the method allows faster execution which is a key parameter for the real-time scene analysis.