CFP last date
22 April 2024
Reseach Article

An Approach to Deformable Object Tracking in Video

Published on November 2013 by Chandra Mani Sharma, Harish Kumar, Deepika Sharma
8th National Conference on Next generation Computing Technologies and Applications
Foundation of Computer Science USA
NGCTA - Number 1
November 2013
Authors: Chandra Mani Sharma, Harish Kumar, Deepika Sharma
885be2e5-3d1e-4864-862f-d0a80974f4fe

Chandra Mani Sharma, Harish Kumar, Deepika Sharma . An Approach to Deformable Object Tracking in Video. 8th National Conference on Next generation Computing Technologies and Applications. NGCTA, 1 (November 2013), 12-16.

@article{
author = { Chandra Mani Sharma, Harish Kumar, Deepika Sharma },
title = { An Approach to Deformable Object Tracking in Video },
journal = { 8th National Conference on Next generation Computing Technologies and Applications },
issue_date = { November 2013 },
volume = { NGCTA },
number = { 1 },
month = { November },
year = { 2013 },
issn = 0975-8887,
pages = { 12-16 },
numpages = 5,
url = { /proceedings/ngcta/number1/14191-1305/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 8th National Conference on Next generation Computing Technologies and Applications
%A Chandra Mani Sharma
%A Harish Kumar
%A Deepika Sharma
%T An Approach to Deformable Object Tracking in Video
%J 8th National Conference on Next generation Computing Technologies and Applications
%@ 0975-8887
%V NGCTA
%N 1
%P 12-16
%D 2013
%I International Journal of Computer Applications
Abstract

Object tracking refers to spotting the presence of one or more objects of interest in each frame of a video. The task of object tracking is mainly useful in visual surveillance and scene understanding applications. With the increasing availability of rich video contents, new application areas are emerging every day. Generally, object tracking plays an intermediate role in many such applications. Several techiques can be found in literature but the basic evaluation and choice parameter that many applications consider include fast execution speed and automatic operation of the tracking method. There remains a tradeoff between accuracy and execution speed for the object tracking methods. Further it is true that in today's applications the most important category of objects is human himself. This paper proposes a technique for human detection and tracking in video. The proposed method is accurate and efficient in execution speed. Several experimental results presented in the paper demonstrate novelty of method.

References
  1. W. Hu and T. Tan, "A Survey on Visual Surveillance of Object Motion and Behaviors ", IEEE Trans. Systems, Man, and Cybernetics, Vol. 34, No. 3, pp. 334-352,2006.
  2. E. Stringa and C. S. Regazzoni, "Real-time Video Shot Detection for Scene Surveillance Application," IEEE Trans. Image Processing, Vol. 9, pp. 69-79, 2009.
  3. I. Haritaoglu, D. Harwood, and L. Davis, "W4: Who? When? Where? What? a Real-time System for Detecting and Tracking People," In the Proc. IEEE Conference on Face Gesture Recognition, pp. 222–227, 1998.
  4. D. Koller, J. Weber, T. Huang, J. Malik, B. Rao G. Ogasawara, and S. Russell, "Toward Robust Automatic Traf?c Scene Analysis in Real-time," In the Proc. IEEE Conference on Pattern Recognition, Vol. 1, pp. 126–131, 1994,.
  5. S. Nigam and A. Khare, "Curvelet Transform Based Object Tracking", In the proc. IEEE Conference on Computer and Communication Technology, pp. 230-235, 2010
  6. P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," In the Proc. of IEEE Int. Conference on Computer vision and Pattern Recognition pp. 511-518, 2001.
  7. Y. Freund, and R. Schapire, " A decision theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences 55, pp. 119–139, 2007
  8. P. Lanvin, J-C. Noyer, M. Benjelloun," Object Detection and Tracking using the Particle Filtering," In the Proc. of IEEE 37th Asilomar Conference on Signals, Systems and Computers, Vol. 2,pp. 1595-1599, 2003.
  9. J. Wang, Y. Ma, C. Li, H. Wang, J. Liu, "An Efficient Multi-Object Tracking Method using Multiple Particle Filters" In the Proc. of World Congress on Computer Science and Information Engineering, pp. 568-572, 2009.
  10. Y. Chen, S. Yu, J. Fan, W. Chen, H. Li, "An Improved Color-based Particle Filter for Object Tracking," In the Proc. of IEEE Second Int. Conference on Genetic and Evolutionary Computing, pp. 360-363, 2008.
  11. F. Bardet, T. Chateau, D. Ramadasan, "Illumination Aware MCMC Particle Filter for Long-term Outdoor Multi-Object Simultaneous Tracking and Classification," In the Proc. of IEEE 12th Int. Conference on Computer Vision, pp. 1623-1630, 2009.
  12. C. Luo, X. Cai, J. Zhang, "Robust Object Tracking using the Particle Filtering and Level Set Methods: A Comparative Experiment," In the Proc. of IEEE 10th Workshop on Multimedia Signal Processing, pp. 359-364, 2008.
  13. P. Brasnett, L. Mihaylova, N. Canagarajah, and D. Bull, "ParticleFiltering for Multiple Cues for Object Tracking in Video Sequences," In the Proc. of the 17th SPIE Annual Symposium on Electronic Imaging, Science and Technology, vol. 5685, pp. 430–440,2005
  14. M. Isard and A. Blake, "A Mixed-State Condensation Tracker with AutmaticModel-Switching," in Proc. Int. Conf. Computer Vision, pp. 107–112, 1998
  15. M. Isard and J. MacCormick, "Bramble: a Bayesian Multiple Blob Tracker," in Proc. Int. Conf. Computer Vision, pp. 34–41, 2001
  16. J. Czyz, B. Ristic, and B. Macq, "A Color-Based Particle Filter for Joint Detection and Tracking of Multiple Objects," In the Proc. of the ICASSP, pp. 217-220, 2005.
  17. T. B. Nguyen and A. Khare, "Object Tracking Of Video Sequence in Curvelet Domain", Int. Journal of Image and Graphics, pp. 1-20, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Human Detection Object Tracking Visual Sueveillance Video Processing