Authors:
Pavel Yakimov
and
Vladimir Fursov
Affiliation:
Samara State Aerospace University and Image Processing Systems Institute of the Russian Academy of Sciences, Russian Federation
Keyword(s):
Traffic Signs Recognition, Advanced Driver Assistance Systems, Graphics Processing Units, Image Processing, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Design and Implementation of Signal Processing Systems
;
Image and Video Processing, Compression and Segmentation
;
Multidimensional Signal Processing
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Telecommunications
Abstract:
Traffic Signs Recognition (TSR) systems can not only improve safety, compensating for possible human
carelessness, but also reduce tiredness, helping drivers keep an eye on the surrounding traffic conditions. This
paper proposes an efficient algorithm for real-time TSR. The article considers the practicability of using HSV
color space to extract the red color. An algorithm to remove noise to improve the accuracy and speed of
detection was developed. A modified Generalized Hough transform is then used to detect traffic signs. The
current velocity of a vehicle is then used to predict the sign’s location in the adjacent frames in a video
sequence. Finally, the detected objects are being classified. The developed algorithms have been tested using
real scene images and the German Traffic Sign Detection Benchmark (GTSDB) dataset and showed efficient
results.