Paper Titles

A Soft Decision Making Model for Detail Preservation
p.323

Optimal Bit Plane Arrangement for Image Size-Performance Tradeoff
p.327

Electromechanical Properties of Vertically Aligned Carbon Nanotube
p.332

Clustering in Wireless Sensor Networks: Performance Comparison of EAMMH and LEACH Protocols Using MATLAB
p.337

Embedded System for Detection, Recognition and Classification of Traffic Signs
p.343

Design and Research of Low-Energy Consumption Sensor Nodes in a Smart-Scene System
p.352

Design and Manufacture of Ultra-Small Chip Power Inductor
p.359

Experimental Investigations upon the Electrical Resistance of Microcontacts
p.365

The Study on Disturbing Force of Dynamic Mechanical Systems Using Kalman Filter Techniques
p.371

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 705Embedded System for Detection, Recognition and...

Embedded System for Detection, Recognition and Classification of Traffic Signs

Article Preview

Abstract:

This study concerns the development of an embedded system with low computational resources and low power consumption. It uses the NXP LPC2106 with ARM7 processor architecture, for acquiring, processing and classifying images. This embedded system is design to detect and recognize traffic signs. Taking into account the processor capabilities and the desired features for the embedded system, a set of algorithms was developed that require low computational resources and memory. These features were accomplished using a modified Freeman Method in conjunction with a new algorithm "ear pull" proposed in this work. Each of these algorithms was tested with static images, using code developed for MATLAB and for the CMUcam3. The road environment was simulated and experimental tests were performed to measure traffic signs recognition rate on real environment. The technical limitations imposed by the embedded system led to an increased complexity of the project, however the final results provide a recognition rate of 77% on road tests. Thus, the embedded system features overcome the initial expectations and highlight the potentialities of both algorithms that were developed.

You might also be interested in these eBooks

MEMS and Mechanics

Info:

Periodical:

Advanced Materials Research (Volume 705)

Pages:

343-351

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.705.343

Citation:

Cite this paper

Online since:

June 2013

Authors:

Diogo Veríssimo Correia, Pedro Dinis Gaspar

Keywords:

Classification, Detection, Recognition, Traffic Signs

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

[1] S.M. Bascón, J.A. Rodríguez, S.L. Arroyo, A.F. Caballero, F.L. Ferreras, An optimization on pictogram identification for the road-sign recognition task using SVMs, Computer Vision and Image Understanding, 114(3) (2010) 373-383.

DOI: 10.1016/j.cviu.2009.12.002

[2] P. Siegmann, R. López, P. Gil, S. Lafuente, S. Maldonado, Fundaments in luminance and retroreflectivity measurement of vertical traffic signs using a color digital camera, IEEE Transactions on Instrumentation and Measurement 57(3) (2008) 607-615.

DOI: 10.1109/tim.2007.911643

[3] R. Vicen-Bueno, R. Gil-Pita, M. Rosa-Zurera, M. Utrilla-Manso, F. López-Ferreras, Multilayer perceptrons applied to traffic sign recognition tasks, Proc. of the 8th Int. Conf. on Artificial Neural Networks: computational Intelligence and Bioinspired Systems (IWANN'05), (2005) 865-872.

DOI: 10.1007/11494669_106

[4] T. Hibi, Vision based extraction and recognition of road sign region from natural color image, by using HSL and coordinates transformation, Proceedings of the 29th International Symposium Automotive Technology and Automation (1996) 201-206.

DOI: 10.1541/ieejias.115.1484

[5] D. Kang, N. Griswold, N. Kehtarnavaz, An invariant traffic sign recognition system based on sequential color processing and geometrical transformation, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (1994) 88-93.

DOI: 10.1109/iai.1994.336679

[6] S.H. Hsu, C.L. Huang, Road sign detection and recognition using matching pursuit method, Image and Vision Computing 19(3) (2001) 119-129.

DOI: 10.1016/s0262-8856(00)00050-0

[7] P. Douville, Real-time classification of traffic signs, Real-Time Imaging 6(3) (2000) 185-193.

DOI: 10.1006/rtim.1998.0142

[8] U. Krebel, F. Lindner, C. Wohler, A. Linz, Hypothesis verification based on classification at unequal error rates, Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN 99) (1999) 874-879.

DOI: 10.1049/cp:19991222

[9] S. Maldonado, S. Lafuente, P. Gil, H. Gomez, F. Lopez, Road-sign detection and recognition based on support vector machines, IEEE Trans. on Intelligent Trans. Systems 8(2) (2007) 264-278.

DOI: 10.1109/tits.2007.895311

[10] Y. Aoyagi, T. Asakura, A study on traffic sign recognition in scene image using genetic algorithms and neural networks, Proceeding of the 22nd IEEE International Conference on Industrial Electronics, Control and Instrumentation, vol. 3, Taipeh, Taiwan (1996) 1838-1843.

DOI: 10.1109/iecon.1996.570749

[11] A. de la Escalera, L.E. Moreno, E.A. Puente, M.A. Salichs, Neural traffic sign recognition for autonomous vehicles, 20th International Conference on Industrial, Electronics, Control and Instrumentation (IECON'94), IEEE, vol. 2 (1994) 84-846.

DOI: 10.1109/iecon.1994.397896

[12] R. Luo, H. Potlapalli, D. Hislop, Neural network based landmark recognition for robot navigation, Proc. of the International Conference on Industrial Electronics, Control, Instrumentation, and Automation, Power Electronics and Motion Control, IEEE (1992) 1084-1088.

DOI: 10.1109/iecon.1992.254461

[13] A. de la Escalera, J.M. Armingol, J.M. Pastor, F.J. Rodríguez, Visual sign information extraction and identification by deformable models for intelligent vehicles, IEEE Transactions on Intelligent Transportation Sytems 15(2) (2004) 57-68.

DOI: 10.1109/tits.2004.828173

[14] E. Perez, B. Javidi, Nonlinear distortion-tolerant filters for detection of road signs in background noise, IEEE Transactions on Vehicular Technology 51(3) (2002) 567-576.

DOI: 10.1109/tvt.2002.1002505

[15] J. Miura, T. Kanda, Y. Shirai, An active vision system for real-time traffic sign recognition, Proceedings of the IEEE Intelligent Transportation Systems (2000) 52-57.

DOI: 10.1109/itsc.2000.881017

[16] E. Frias-Martinez, A. Sanchez, J. Valez, Support vector machines versus multilayer perceptrons for efficient off-line signature recognition, Eng. App. of Art. Intelligence 19(6) (2006) 693-704.

DOI: 10.1016/j.engappai.2005.12.006

[17] B. Cyganek, Circular road signs recognition with soft classifiers, Integrated Computer-Aided Engineering 14(4) (2007) 323-343.

DOI: 10.3233/ica-2007-14404

[18] H. Shojania, Real-time Traffic Sign Detection (2003). Available from: http://hassan.shojania.com/

[19] A. de la Escalera, J.M. Armingol, M. Mata, Traffic sign recognition and analysis for intelligent vehicles, Image and Vision Computing 21(3) (2003) 247-258.

DOI: 10.1016/s0262-8856(02)00156-7

[20] P. Jones, M. Viola, Rapid Object Detection using a Boosted Cascade of Simple Features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, IEEE (2001) 511-518.

DOI: 10.1109/cvpr.2001.990517

[21] A. Rowe, A., Goode, CMUcam3 software installation guide v0.1 for the CMUcam3 embedded vision sensor, Illah Nourbakhsh, Carnegie Mellon University, 2006.

[22] MathWorks, Introduction to MATLAB, MathWorks, 2008.

[23] Cygwin, Cygwin User's Guide, Red Hat Inc., 2010.

[24] S. Mariano, Sistemas de decisão óptima em coordenação hidrotérmica para planeamento operacional, Universidade da Beira Interior, 2000. (in portuguese)

[25] A. Lorsakul, J. Suthakorn, Traffic sign recognition using neural network on OpenCV: Toward intelligent vehicle/driver assistance system, Mahidol University, 2007.