An Enhanced Traffic Sign Detection and Recognition System Using Covariance Matrix Descriptor
Date Issued
2011
Date
2011
Author(s)
Lee, Cheng-Wei
Abstract
Intelligent vehicle (IV) systems have gathered great importance in recent year. Many driver assistance systems have been developed to improve driving safety. Traffic sign recognition system is an important subsystem of driver assistance system because it can remind the drivers of the road sign information. The proposed system comprises four modules: preprocessing, training, detection and recognition. In detection phase, a sliding window is applied to the test image in different scales. For each sliding window, we compute covariance matrix descriptor for feature extraction, and determine whether it is a sign or not. Moreover, in order to reduce computational time and false positive rate, the detector is built by Adaboost algorithm and the cascaded decision. In recognition phase, we perform the sign identification by using multi- class Support Vector Machine (SVM). The proposed algorithms were tested in sunny conditions and four different noisy outdoor scenes: occluded, faded, backlight and blurred conditions. From the experimental results, the proposed system shown high performance to detect and recognize traffic signs.
Subjects
traffic sign detection and recognition
covariance matrix descriptor
Adaboost algorithm
multi-class SVM
Type
thesis
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