Trainable classifier-fusion schemes: An application to pedestrian detection

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Volumn , Issue , 2009, Pages 432-437

  Junior, Oswaldo Ludwig ^a   Delgado, David ^a   Gonçalves, Valter ^a   Nunes, Urbano ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN; CLASSIFIER COMBINATION; COMPARATIVE STUDIES; DAIMLER-CHRYSLER; DATA SETS; FALSE POSITIVE RATES; FEATURE EXTRACTOR; HEURISTIC RULES; PEDESTRIAN DETECTION;

CLASSIFIERS; HEURISTIC ALGORITHMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; LEARNING SYSTEMS; VEHICLE LOCATING SYSTEMS;

LEARNING ALGORITHMS;

EID: 72449198559     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ITSC.2009.5309700     Document Type: Conference Paper

References (18)

1. Freund, Y. Schapire, R., "Experiments with a new boosting algorithm" Proceedings of the Thirteenth International Conference on Machine Learning, 148-156 Bari, 1996.
2. Breiman, L., "Bagging predictors", Machine Learning, 24(2), 123-140, 1996.
3. Munder, S. and Gavrila D.M., "An Experimental Study on Pedestrian Classification", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 11, November 2006.
4. Lin, T; Zha, H, "Riemannian Manifold Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30 (5), May 2008.
5. Lowe, D., Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, pp. 91110, 2004.
6. Zhu, Q., Avidan, Q., Yeh, M.-C., and Cheng, K.-T., Fast human detection using a cascade of histograms of oriented gradients, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, June 2006.
7. Dalal, N. and Triggs, B., Histograms of oriented gradients for human detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 886893.
8. Tuzel, O., Porikli, F., and Meer, P., A fast descriptor for detection and classification, Proc. 9th European Conf. on Computer Vision, 2006.
9. Porikli, F., Tuzel, O., and Meer, P., Covariance Tracking using Model Update Based on Lie Algebra, IEEEConf. on Computer Vision and Pattern Recognition, 2006.
10. Tuzel, O., Porikli, F., and Meer, P., Human Detection via Classification on Riemannian Manifolds, Proc. CVPR, pp 1-8, 2007
11. Papageorgiou, C., Evgeniou, T., and Poggio, T., A trainable pedestrian detection system, in Proceedings of Intelligent Vehicles Symposium, October 1998, pp. 241246.
12. Vapnik, V.N., Statistical Learning Theory, John Wiley and Sons, 1998.
13. Ludwig O. and Nunes, U. Improving the Generalization Properties of Neural Networks: an Application to Vehicle Detection. in 11th International IEEE Conference on Intelligent Transportation Systems - ITSC 2008, October 12-15, Beijing, China.
14. Nanni, L. and Lumini, A., "Ensemble of Multiple Pedestrian Representations", IEEE Transactions on Intelligent Transportation Systems, Vol. 9, June 2008, pp. 365-369.
15. Liang, Y-h., Wang, Z-h., Guo, S., Xu, X-w. and Cao, X-y., "Pedestrian Detection using KPCA and FLD Algorithms", IEEE International Conference on Automation and Logitics, Aug. 2007, pp. 1572-1575.
16. S. Paisitkriangkrai, C. Shen, and J. Zhang, "An experimental evaluation of local features for pedestrian classification," in Proc. Int. Conf. Digital Image Computing - Techniques and Applications, Adelaide, Australia, 2007.
17. Cao, H., Naito, T. and Ninomiya, Y., "Approximate RBF Kernel SVM and Its Applications in Pedestrian Classification", MLVMA'08, Sep. 2008.
18. P. Dollr, Z. Tu, H. Tao, and S. Belongie, "Feature mining for image classification," in IEEE Conf. Comp. Vis. Pattern Recognit., CVPR'2007, Minneapolis, MN, Jun. 2006.