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Improving the Performance of Human Detection Technique using Cascaded Support Vector Machine

by Raksha Tomar, Murlidhar Vishwakarma, Ravi Singh Pippal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 3
Year of Publication: 2015
Authors: Raksha Tomar, Murlidhar Vishwakarma, Ravi Singh Pippal
10.5120/20531-2872

Raksha Tomar, Murlidhar Vishwakarma, Ravi Singh Pippal . Improving the Performance of Human Detection Technique using Cascaded Support Vector Machine. International Journal of Computer Applications. 117, 3 ( May 2015), 1-4. DOI=10.5120/20531-2872

@article{ 10.5120/20531-2872,
author = { Raksha Tomar, Murlidhar Vishwakarma, Ravi Singh Pippal },
title = { Improving the Performance of Human Detection Technique using Cascaded Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 3 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number3/20531-2872/ },
doi = { 10.5120/20531-2872 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:18.959567+05:30
%A Raksha Tomar
%A Murlidhar Vishwakarma
%A Ravi Singh Pippal
%T Improving the Performance of Human Detection Technique using Cascaded Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 3
%P 1-4
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Human detection plays an important role in security surveillance and computer vision. The process of human detection is very complex due to variant feature of human such as color, texture and shape and size. The process of feature extraction imparts a major role in human detection technique. Now a days used classification technique to define the feature of human. The classification process define the pattern of feature for the process of detection, the process of features generates a bag of feature for the process of classification technique. In this paper improved the support vector machine classification technique for the classification of human detection. The improved support vector machine is called cascaded support vector machine. The cascading of support vector machine improved the process of human detection. Our proposed algorithm implemented in MATLAB 7. 8. 0 software and used human video of different location. Our empirical evaluation of experimental result shows that the proposed methods give a better result in compression of support vector machine classifier.

References
  1. Qixiang Ye, Zhenjun Han, Jianbin Jiao, Jianzhuang Liu "Human Detection in Images via Piecewise Linear Support Vector Machines" Ieee Transactions On Image Processing, Vol-22, 2013. Pp 778-790.
  2. Paul Viola,Michael J. Jones,Daniel Snow "Detecting Pedestrains Using Patterns of Motion and Appearance" in international conference on computer vision (ICCV) Oct 2003
  3. SubhransuMaji, Alexander C. Berg "Classification using Intersection Kernel Support Vector Machines is Efficient" IEEE, 2008. Pp 234-240.
  4. Navneet Dalal , Bill Triggs and Cordelia Schmid " Human Detection using Oriented Histograms of Flow and Apperence" European Conference on Computer Vision(ECCV'06)2006 Pp. 428-441
  5. Ran Xu, Baochang Zhang, Qixiang Ye, Jianbin Jiao "Cascaded L1-norm Minimization Learning (CLML) Classifier for Human Detection" in Proc. IEEE Int. Conf. Compute. Vis. Pattern Recognit. , Jun. 2010, Pp 89–96.
  6. Frank de Morsier, DevisTuia, Maurice Borgeaud, olkerGass, Jean-Philippe Thiran "Semi-Supervised Novelty Detection using SVM entire solution path" IEEE, 2013. Pp 1- 12.
  7. Christoph H. Lampert "An Efficient Divide-and-Conquer Cascade for Nonlinear Object Detection" IEEE, 2012. Pp 1-8.
  8. Ofir Pele, Ben taskar, Amir Globerson, Michael Werman "The Pairwise Piecewise-Linear Embedding for Eÿcient Non- Linear Classification" Proceedings of the 30 th International Conference on Ma-chine Learning, Atlanta, Georgia, USA, 2013. Pp 1-9.
  9. Koheiogawa, masashisugayama "Infinitesimal annealing for training semi supervised support vector machines" Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. Pp 15-23.
  10. Sreedevi M, Yaswanth Kumar Avulapati, Anjan Babu G, Sendhil Kumar R "Real Time Movement Detection for Human Recognition" the World Congress on Engineering and Computer Science 2012 Vol I WCECS 2012, October 24-26, 2012, San Francisco, USA.
  11. Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, TomasoPoggio "Robust Object Recognition with Cortex-Like Mechanisms" IEEE transaction On pattern Analysis And machine Intelligence , VOL-29, 2007. Pp 411-429.
  12. Markus Enzweiler, Dariu M. Gavrila "A Multi-Level Mixture-of-Experts Framework for Pedestrian Classification" IEEE, 2011. Pp 1-13.
  13. Xiaoyu Wang, Tony X. Han, Shuicheng Yan "An HOGLBP Human Detector with Partial Occlusion Handling" IEEE, 2011. Pp 456-463.
  14. Gupta, S. K. ;Agrwal,S. ; Meena, Y. K. ; Nain,N "A Hybrid Of Feature Extraction for Facial Expression Recognition" IEEE, Dec 2011. Pp 422-425.
  15. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. , Jun. 2005, pp. 886–893.
Index Terms

Computer Science
Information Sciences

Keywords

Human detection feature extraction SVM

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