doi: 10.7763/IJCTE.2010.V2.226
A Robust Method Applied to Human Detection
Seyyed Meysam Hosseini1
,
Hasan Farsi2,3
- 1Islamic Azad University Of Firoozkooh, electrical engineering group, Iran
- 2University Of Birjand, Iran
- 3Department of Electrical Engineering, Birjand, Iran.
Abstract
PC-SVM is a new developed support vector machine classifier with probabilistic constrains which presence of samples probability in each class is determined based on a distribution function. The presence of noise causes incorrect calculation of support vectors thereupon margin can not be maximized. In the Pc-SVM, constraints boundaries and constraints occurrence have probability density functions which it helps for achieving maximum margin. The main target of this paper is introducing a robust visual object recognition based on PC- SVM. Human detection is used as benchmark problem for the proposed algorithm. Experimental results show superiority of the probabilistic constraints support vector machine (PC-SVM) relative to standard SVM in human detection.
Keywords
- pc-svm
- human detection
- histograms of oriented gradients
How to Cite
Seyyed Meysam Hosseini and Hasan Farsi, "A Robust Method Applied to Human Detection," International Journal of Computer Theory and Engineering, vol. 2, no. 5, pp. 692-694, 2010. https://doi.org/10.7763/IJCTE.2010.V2.226
Copyright & License
Copyright © 2010 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).