Pedestrian detection using KPCA and FLD algorithms

Proceedings of the IEEE International Conference on Automation and Logistics, ICAL 2007

Volumn , Issue , 2007, Pages 1572-1575

  Liang, Ying Hong ^a,b   Wang, Zhi Yan ^a   Guo, Sen ^b   Xu, Xiao Wei ^a   Cao, Xiao Ye ^a  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY   (China)

Author keywords

Feature extraction; Fisher linear discriminant; Kernel function; Kernel principle component analysis; Pedestrian detection

Indexed keywords

ALGORITHMS; BENCHMARKING; FEATURE EXTRACTION; PEDESTRIAN SAFETY; PRINCIPAL COMPONENT ANALYSIS;

FISHER LINEAR DISCRIMINANT (FLD); KERNEL FUNCTION;

OBJECT RECOGNITION;

EID: 40649085370     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAL.2007.4338822     Document Type: Conference Paper

References (16)

1. S. Munder and D.M. Gavrila, "An Experimental Study on Pedestrian Classification," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1863-1868, 2006.
2. C. Papageorgiou, T. Evgeniou, and T. Poggio, "A trainable pedestrian detection system," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 241-246, 1998.
3. Constantine Papageorgiou, Tomaso Poggio, "A Trainable System for Object Detection," International Journal of Computer Vision, vol. 38, no. 1, pp. 15-33, 2000.
4. Paul Viola, Michael J. Jones, and Daniel Snow, "Detecting Pedestrians Using Patterns of Motion and Appearance," International Journal of Computer Vision, vol. 63, no. 2, pp. 153-161, 2005.
5. Ying Wu, Ting Yu and Gang Hua, "A Statistical Field Model for Pedestrian Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1023-1030, 2005.
6. M. A. Sotelo, I. Parra, D. Fern'andez and E. Naranjo, "Pedestrian Detection using SVM and Multi-feature Combination," Proceedings of the IEEE Intelligent Transportation Systems Conference, pp. 103-108, 2006.
7. Edgar Seemann, Bastian Leibe, Krystian Mikolajczyk and Bernt Schiele, "An Evaluation of Local Shape-Based Features for Pedestrian Detection," Proceedings of the British Machine Vision Conference, 2005.
8. A. Lipton, H. Fujiyoshi, and R. Patil, "Moving Target Classification and Tracking from Real-Time Video," Proceedings of the DARPA Image Understanding Workshop, pp. 8-14, 1998.
9. F. Suard, A. Rakotomamonjy, A. Benshrair, A. Broggi, "Pedestrian Detection using Infrared images and Histograms of Oriented Gradients," IEEE Symposium on Intelligent Vehicule, pp. 206-212, 2006.
10. H. Nanda and L. Davis, "Probabilistic template based pedestrian detection in infrared videos," Proceedings of the IEEE Intelligent Vehicles Symposium, Vol. 1, pp. 15-20, 2002.
11. X. Liu and K. Fujimura, "Pedestrian detection using stereo night vision," IEEE Trans. on Vehicular Technology, vol. 53, no. 6, pp. 1657-1665, 2004.
12. C. Hilario, J. M. Collado, J. M" Armingol, A. de la Escalera, "Pedestrian Detection for Intelligent Vehicles based on Active Contour Models and Stereo Vision," Lecture Notes in Computer Science, vol. 3643, pp. 537-542, 2005.
13. A.K. Jain, R.P.W. Duin, and J. Mao, "Statistical Pattern Recognition: A Review," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, 2000.
14. Jian Yang, Zhong Jin, Jing-Yu Yang, David Zhang, Alejandro F. Frangi, "Essence of kernel Fisher discriminant: KPCA plus LDA," Pattern. Recognition, vol. 37, no. 10, pp. 2097-2100, 2004
15. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K.-R. Muller, "Fisher discriminant analysis with kernels," IEEE International Workshop on Neural Networks for Signal Processing, vol. IX, no. 8, Madison, pp. 41-48, 1999.
16. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.