An evolutionary support vector machines classifier for pedestrian detection

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2006, Pages 4223-4227

  Chen, D ^a   Cao, X B ^a   Xu, Y W ^a   Qiao, H ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b INSTITUTE OF AUTOMATION   (China)

Author keywords

Classifier; Genetic algorithm; Pedestrian detection system; Redial base function; Support vector machine

Indexed keywords

CODES (SYMBOLS); GENETIC ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; PARAMETER ESTIMATION; SUPPORT VECTOR MACHINES;

EVOLUTIONARY SVM CLASSIFIERS; PEDESTRIAN DETECTION; PEDESTRIAN DETECTION SYSTEMS; REDIAL BASE FUNCTIONS;

CLASSIFICATION (OF INFORMATION);

EID: 34250642831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2006.281917     Document Type: Conference Paper

References (11)

1. U. Franke et al, "Autonomous driving goes downtown," in IEEE Intelligent System, pp. 32-40, November 1998.
2. D. M. Gavrila and J. Geibel, "Shape-based pedestrian detection and tracking," in Proc. IEEE Intelligent Vehicles Symposium, Versailles, France,June 2002.
3. T. Evgeniou C. Papageorgiou and T. Poggio. "A trainable pedestrian detection system," in IEEE Intelligent Vehicle Symposium, 1998.
4. Grant Grubb, Alexander Zelinsky Lars Nilsson, Magnus Rilbe "3D Vision Sensing for Improved Pedestrian Safety," in IEEE Intelligent Vehicle Symposium, 2004.
5. Fengliang Xu, Xia Liu, and Kikuo Fujimura "Pedestrian Detection and Tracking With Night Vision," in IEEE Transactions on Intelligent Transportation System, vol. 6, no.l, march 2005
6. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, 1995.
7. O. Chapelle, V. Vapnik, O. Bousquet, et al. "Choosing multiple parameters for support vector machines," Machine Learning, pp: 131159, 2002.
8. K. Tsuda, S. Mika, et al. "Learning to predict the leave-one-out error of kernel based classifiers" Proc. 2001 Int'l Conf. Artificial Neural Networks-ICANN 20011 Berlin: Springer-Verlag, 2001
9. Joachims T. "Estimating the generalization performance of a SVM efficiently ·"Proceedings of the 17th International Conference on Machine Learning. San Francisco: Morgan, pp: 431-438, 2000.
10. C.J.C. Bugres, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998.
11. T. Joachims, "Making large-scale SVM learning practical," in Advances in Kernel Methods-Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press, (1998)