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PROJECT TITLE :

Traffic Sign Detection and Recognition - 2015

ABSTRACT:

This paper proposes a unique system for the automatic detection and recognition of traffic signs. The proposed system detects candidate regions as maximally stable extremal regions (MSERs), which offers robustness to variations in lighting conditions. Recognition relies on a cascade of support vector machine (SVM) classifiers that were trained using histogram of oriented gradient (HOG) features. The coaching knowledge are generated from artificial template pictures that are freely available from an on-line database; thus, real footage road signs are not required as training information. The proposed system is accurate at high vehicle speeds, operates beneath a vary of climatic conditions, runs at a mean speed of twenty frames per second, and acknowledges all categories of ideogram-based mostly (nontext) traffic symbols from an on-line road sign database. Comprehensive comparative results to illustrate the performance of the system are presented.


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Road Traffic Object Detection Image Classification Support Vector Machines Computer Vision Visual Databases Character Recognition Lighting Real-Time Systems Traffic Sign Recognition Histogram Of Oriented Gradient (Hog) Features Maximally Stable Extremal Regions (Msers) Support Vector Machines (Svms) Synthetic Data
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