We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
An Efficient Restoration Method for the Faint Dot Matrix Images

Ao Zhu Peng Cao
Random Articles
Reseach Article

Certain Approach of Object Tracking using Optical Flow Techniques

by R. Revathi, M. Hemalatha
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 53 - Number 8
Year of Publication: 2012
Authors: R. Revathi, M. Hemalatha
10.5120/8445-2232

R. Revathi, M. Hemalatha . Certain Approach of Object Tracking using Optical Flow Techniques. International Journal of Computer Applications. 53, 8 ( September 2012), 50-57. DOI=10.5120/8445-2232

@article{ 10.5120/8445-2232,
author = { R. Revathi, M. Hemalatha },
title = { Certain Approach of Object Tracking using Optical Flow Techniques },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 53 },
number = { 8 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 50-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume53/number8/8445-2232/ },
doi = { 10.5120/8445-2232 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:53:38.525823+05:30
%A R. Revathi
%A M. Hemalatha
%T Certain Approach of Object Tracking using Optical Flow Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 53
%N 8
%P 50-57
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day's object tracking is more difficult and tricky to surveillance in real time. This proposed work deals with the tracking of moving object in a sequence of frames and it also determines the velocity of the object. In this work algorithms are developed for improving the image quality, segmentation, feature extraction and for identifying the velocity. The algorithms developed are implemented and evaluated using MATLAB. The image quality of the video frame is obtained by applying certain noise removal filters. Next, identifying the moving objects from the portion of the video frame is performed using the background subtraction technique based on frame difference. The object tracking is performed by optical flow with Bayesian boosting algorithm method on detected object in each frame as a feature extraction method. There are several papers describing the accomplishment of optical flow. Some results are adequate, but in many projects, there are restrictions. In most preceding applications, because the camera is typically static, it is simple to apply optical flow to identify the moving targets in a scene and get their trajectories. When the camera moves a global motion will be added to the local motion, which complicates the issue. In this work we used the combination of boosting algorithm called Bayesian boosting is adopted to improve the performance of Optical flow (OFTBB). The distance traveled by the object is determined using its centroid pixel. It is calculated by using the Euclidean distance formula. Then the velocity of the object is calculated by finding the object moved in distance in a sequence of frames with respect to the video frame rate.

References
  1. Peter Mountney, Danail Stoyanov and Guang-Zhong Yang (2010). "Three-Dimensional Tissue Deformation Recovery and Tracking: Introducing techniques based on laparoscopic or endoscopic images. " IEEE Signal Processing Magazine. 2010 July. Volume: 27". IEEE Signal Processing Magazine 27 (4): 14–24. DOI:10. 1109/MSP. 2010. 936728.
  2. Lyudmila Mihaylova, Paul Brasnett, Nishan Canagarajan and David Bull (2007). Object Tracking by Particle Filtering Techniques in Video Sequences; In: Advances and Challenges in Multisensor Data and Information. NATO Security Through Science Series, 8. Netherlands: IOS Press. pp. 260–268. ISBN 978-1-58603-727-7.
  3. Kato, Hirokazu, and Mark Billinghurst (1999). "Marker Tracking and HMD Calibration for a Video-based Augmented Reality Conferencing System". IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality (IEEE Computer Society, Washington, DC, USA).
  4. A. Gyaourova, C. Kamath,S. and C. Cheung-Block Matching object tracking-LLNL Technical report, October 2003.
  5. Y. Rosenberg and M. Weman-Real Time Object Tracking from a Moving Video Camera: A software approach on PC-Applications of Computer Vision ,, 1998. WACV '98. Proceedings.
  6. A. Turolla,L. Marchesotti and C. S. Regazzoni- Multicamera Object tracking in video surveillance applications.
  7. Y. Wang,J. Doherty and R. Van Dyck-Moving object tracking in video-Proc. Conference on Information Sciences and Systems,Princeton,NJ,March2000.
  8. Ci gdem Ero glu Erdem and Bulent San-Video Object Tracking With Feedback of Performance Measures-IEEE Transactions on circuits and systems for video technology,vol. 13,no. 4,April 2003.
  9. Alok K. Watve and Shamik Sural – A Seminar on Object tracking in Video Scenes.
  10. P. Subashini,M. Krishnaveni,Vijay Singh-Implementation of Object Tracking System Using Region Filtering Algorithm based on Simulink Blocksets,International Journal of Engineering Science and Technology(IJEST),Vol. 3 No. 8 August 2011. PP-6744-6750. ISSN:0975-5462.
  11. Ashwani Aggarwal,Susmit Biswas,sandeep Singh,Shamik Sural and A. K. Majundar,-Object tracking using background subtraction and motion estimation in MPEG videos,Springer-Verlag Berlin Heidelberg,ACCV 2006,LNCS 3852,pp:121-130.
  12. Zhuohua Duan,Zixing Cain ad Jinxia Yu,"Occlusion detection and recovery in video object tracking based on adaptive particle filters,"IEEE trans. China,PP. 466-469[Chinese Control and Decision Conference CCDC2009].
  13. XI Tao,Zhang shengxiu and Yan shiyuan,"A robust visual tracking approach with adaptive particle filtering,"IEEE Second Intl. Conf. on Communication Software and Networks 2010,pp:549-553.
  14. Xiaoqin Zhang,Weiming Hu,Zixiang zhao,Yan-guo Wang,Xi Li and Qingdi Wei,"SVD based kalman particle filter for robust visual tracking," IEEE 2008,978-1-4244-2175-6.
  15. Li Ying-hong,Pang Yi-gui,Li Zheng-xi and Liu Ya-li,"An intelligent tracking technology based on kalman and mean shift algorithm," IEEE Second Intl. Conf. on Computer Modeling and Simulation 2010.
  16. Madhur Mehta,Chandni Goyal,M. C. Srivastava and R. C. Jain,"Real time object detection and tracking:Histogram matching and kalman filter approach," IEEE 2010,978-1-4244-5586-7.
  17. A. Purushothaman,K. R. Shankar kumar,R. Rangarajan,A. Kandasawamy-"Compressed Novel Way of Tracking Moving Objects in Image and Video Scenes,"European Journal of Scientific Research ,Vol. 64 No. 3(2011),pp. 353-360. ISSN 1450-216X.
  18. G. Suresh, P. Epsiba, Dr. M. Rajaram, Dr. S. N. Sivanandam," Image And Video Coding With A New Wash Tree Algorithm For Multimedia Services", Journal of Theoretical and Applied Information Technology, 2005 – 2009, PP: 53-59.
  19. T. Senthil Kumar, S. N. Sivanandam," A Modified Approach for Detecting Car in video using Feature Extraction Techniques", European Journal of Scientific Research, ISSN 1450-216X Vol. 77 No. 1 (2012), pp. 134-144.
  20. Daniel Marcus jang,Matthew Turk,"CarRec: A Real Time Car Recognition System", WACV '11 Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision.
  21. Luo Juan, Oubong Gwun "A Comparison of SIFT, PCA-SIFT and SURF", International Journal of Image Processing (IJIP), september 10,2008.
  22. T. Senthil kumar, Dr. S. N. Sivanandam, Adheen Ajay, and P. Krishnakuma," An improved approach for Character Recognition in Vehicle Number plate using Eigenfeature Regularisation and Extraction Method", International Journal of Research and Reviews in Electrical and Computer Engineering (IJRRECE) Vol. 2, No. 2, June 2012, ISSN: 2046-5149.
  23. X. Q. Ding, "Machine printed Chinese character recognition," in Handbook of Character Recognition and Document Image Analysis, H. Bunke and P. S. P. Wang, Beijing: World Scientific Publishing Company, 1997, pp. 305-329
  24. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender.
Index Terms

Computer Science
Information Sciences

Keywords

Object tracking Preprocessing Segmentation Feature Extraction Tracking and Detection

Powered by PhDFocusTM