Abstract:
The recognition of traffic signs is a crucial
component of driver assistance systems that have
been extensively researched worldwide. However,
it remains a challenging issue due to the
increasing number of vehicles, road signs, and the
lack of awareness among drivers and other road
users. A Traffic Sign Recognition (TSR) system is
an advanced autonomous technology designed to
assist drivers by accurately identifying and
interpreting traffic signs. This system plays a
crucial role in enhancing driver awareness and
ensuring appropriate responses to various traffic
conditions. The precise recognition of traffic signs
is
essential for maintaining road safety and
improving the overall driving experience. This
study focuses on the recognition of Sri Lankan
traffic signs and examines the combination of
classifiers with a specific feature extractor. A
dataset of 300 images of road signs was utilized
for this study by capturing the images. The Scale
Invariant Feature Transform (SIFT) was used as a
feature descriptor in this process. The classifiers
employed were Support Vector Machine (SVM)
and k-Nearest Neighbor (k-NN). Different
combinations of SVM and k-NN were applied to the
dataset, and the study achieved 100% accuracy
with various combinations of k-NN. The study
found that the combination of SIFT and SVM is the
most effective method for the proposed recognition
of traffic signs.
