TY - GEN
T1 - Real-time GPU based road sign detection and classification
AU - Ugolotti, Roberto
AU - Nashed, Youssef S.G.
AU - Cagnoni, Stefano
PY - 2012/9/24
Y1 - 2012/9/24
N2 - This paper presents a system for detecting and classifying road signs from video sequences in real time. A model-based approach is used in which a prototype of the sign to be detected is transformed and matched to the image using evolutionary techniques. Then, the sign detected in the previous phase is classified by a neural network. Our system makes extensive use of the parallel computing capabilities offered by modern graphics cards and the CUDA architecture for both detection and classification. We compare detection results achieved by GPU-based parallel versions of Differential Evolution and Particle Swarm Optimization, and classification results obtained by Learning Vector Quantization and Multi-layer Perceptron. The method was tested over two real sequences taken from a camera mounted on-board a car and was able to correctly detect and classify around 70% of the signs at 17.5 fps, a similar result in shorter time, compared to the best results obtained on the same sequences so far.
AB - This paper presents a system for detecting and classifying road signs from video sequences in real time. A model-based approach is used in which a prototype of the sign to be detected is transformed and matched to the image using evolutionary techniques. Then, the sign detected in the previous phase is classified by a neural network. Our system makes extensive use of the parallel computing capabilities offered by modern graphics cards and the CUDA architecture for both detection and classification. We compare detection results achieved by GPU-based parallel versions of Differential Evolution and Particle Swarm Optimization, and classification results obtained by Learning Vector Quantization and Multi-layer Perceptron. The method was tested over two real sequences taken from a camera mounted on-board a car and was able to correctly detect and classify around 70% of the signs at 17.5 fps, a similar result in shorter time, compared to the best results obtained on the same sequences so far.
KW - Differential Evolution
KW - GPGPU
KW - Learning Vector Quantization
KW - Neural Networks
KW - Particle Swarm Optimization
KW - Road Sign Classification
UR - http://www.scopus.com/inward/record.url?scp=84866423621&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866423621&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32937-1_16
DO - 10.1007/978-3-642-32937-1_16
M3 - Conference contribution
AN - SCOPUS:84866423621
SN - 9783642329364
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 153
EP - 162
BT - Parallel Problem Solving from Nature, PPSN XII - 12th International Conference, Proceedings
T2 - 12th International Conference on Parallel Problem Solving from Nature, PPSN 2012
Y2 - 1 September 2012 through 5 September 2012
ER -