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Clustering of Human Beings in an Image and Comparing the Techniques on the basis of Accuracy and Time

by Dipen Saini, Ramandeep Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 6
Year of Publication: 2016
Authors: Dipen Saini, Ramandeep Kaur
10.5120/ijca2016909174

Dipen Saini, Ramandeep Kaur . Clustering of Human Beings in an Image and Comparing the Techniques on the basis of Accuracy and Time. International Journal of Computer Applications. 139, 6 ( April 2016), 39-45. DOI=10.5120/ijca2016909174

@article{ 10.5120/ijca2016909174,
author = { Dipen Saini, Ramandeep Kaur },
title = { Clustering of Human Beings in an Image and Comparing the Techniques on the basis of Accuracy and Time },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 6 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number6/24497-2016909174/ },
doi = { 10.5120/ijca2016909174 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:15.220660+05:30
%A Dipen Saini
%A Ramandeep Kaur
%T Clustering of Human Beings in an Image and Comparing the Techniques on the basis of Accuracy and Time
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 6
%P 39-45
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data clustering is a method in which we make cluster of objects that are somehow similar in characteristics. The criterion for checking the similarity is implementation dependent. If data has some meaning and it corresponds to a human being, than how we can group it in an image or a video. In this research work, we have used three types of algorithms for group detection in an image. In this paper three types of algorithms are used in group detection which detects groups in an image linear dimensionally.

References
  1. M. Thonnat and N. Rota, “Image understanding for visual surveillance application,” Third international workshop on cooperative distributed vision CDV-WS’99, Kyoto, Japan, pp. 51–82, Nov. 1999.
  2. I. Haritaoglu, D. Harwood, and L. Davis, “W4: Who, when, where, what: A realtime system for detecting and tracking people”, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 222–227, April 1998.
  3. I. Haritaoglu, D. Harwood, and L.S. Davis, “Hydra: Multiple people detection and tracking using silhouettes”, IEEE International Workshop on Visual Surveillance, pp. 6–13, June 1999.
  4. D. Koller, J. Weber, and J. Malik, “Robust multiple car tracking with occlusion reasoning”, European Conference on Computer Vision, pp. 189–196, May 1994.
  5. N. Oliver, B. Rosario, and A. Pentland, “A Bayesian computer vision system for modeling human interactions”. International Conference on Vision Systems, January 1999.
  6. V.I. Pavlovic, R. Sharma, and T.S. Huang. Visual interpretation of hand gestures for humancomputer interaction: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, pp. 677–695, 1997.
  7. A. Baumberg and D. Hogg, “An efficient method for contour tracking using active shape models”, IEEE Workshop on Motion of Nonrigid and Articulated Objects, pp.194–199, November 1994.
  8. N. Johnson and D. Hogg, “Learning the distribution of object trajectories for event recognition”, Image and Vision Computing, 14, pp. 609–615, 1996.
  9. C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “Pfinder: Realtime tracking of the human body”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, pp. 780–785, 1997.
  10. S.S. Intille, J.W. Davis, and A.F. Bobick, “Realtime closed world tracking”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 697–703, June 1997.
  11. S.J. McKenna and S. Gong. Recognizing moving faces. In H. Wechsler, P. J. Phillips, V. Bruce, and F. Fogelman Soulie, editors, “Face Recognition: From Theory to Applications”, NATO ASI Series F, vol. 163, 1998.
  12. A.J. Lipton, H. Fujiyoshi, and R.S. Patil, “Moving target classification and tracking from realtime video”, DARPA Image Understanding Workshop, pp. 129–136, November 1998.
  13. T. Darrell, G. Gordon, M. Harville, and J.Woodfill, “Integrated person tracking using stereo, color, and pattern detection”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 601–609, June 1998.
  14. C. Bregler, “Learning and recognizing human dynamics in video sequences”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 568–574, June 1997.
  15. P.I. Wilson and J.Fernandez, “Facial feature detection using Haar classifiers”. Journal of Computing Sciences in Colleges archive, pp. 127-133, April 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Data clustering Digital image group detection features extraction.

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