Improving the Performance of Video Tracking Using SVM

Improving the Performance of Video Tracking Using SVM

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-11 Number-3                          
Year of Publication : 2014
Authors : Ms. G.Anusha , Mrs. E. Golden Julie
  10.14445/22315381/IJETT-V11P226

Citation 

Ms. G.Anusha , Mrs. E. Golden Julie. "Improving the Performance of Video Tracking Using SVM", International Journal of Engineering Trends and Technology (IJETT), V11(3),133-139 May 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Object tracking is the process of separating the moving object from the video sequences. Tracking is essentially a matching problem in object tracking. In order to avoid this matching problem, object recognition is done on the tracked object. . Kalman filter cannot separate the moving object in multiple object tracking so we use background separation techniques. . Background separation algorithm separate moving object from the background based on white and black pixels .Support Vector Machines classifier is used to recognize the tracked object. SVM classifier are supervised learning that associates with machine learning algorithm that analyse and recognize the data used for classification.

References

[1] “What Are We Tracking: A Unified Approach of Tracking and Recognition” Jialue Fan, Xiaohui Shen, Student Member, IEEE, and Ying Wu, Senior Member, IEEE
[2] B. Babenko, M.-H. Yang, and S. Belongie, “Visual tracking with online multiple instance learning,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2009, pp. 983–990.
[3] H. Grabner, M. Grabner, and H. Bischof, “Real-time tracking via on-line boosting,” in Proc. British Mach. Vis. Conf., 2006, pp. 1–10.
[4] Y. Li, H. Ai, T. Yamashita, S. Lao, and M. Kawade, “Tracking in low frame rate video: A cascade particle filter with discriminative observers of different life spans,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30,no. 10, pp. 1728–1740, Oct. 2008.
[5] B. Wu and R. Nevatia, “Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors,” Int. J. Comput. Vis., vol. 75, no. 2, pp. 247–266, Nov. 2007.
[6] C. Huang, B. Wu, and R. Nevatia, “Robust object tracking by hierarchical association of detection responses,” in Proc. Eur. Conf. Comput. Vis., 2008, pp. 788–801.
[7] B. Leibe, K. Schindler, N. Cornelis, and L. V. Gool, “Coupled object detection and tracking from static cameras and moving vehicles,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1683–1698, Oct. 2008.
[8] A. Yilmaz, O. Javed, and M. Shah, “Object tracking: A survey,” ACM Comput. Surv., vol. 38, no. 4, pp. pp. 1–13, 2006.
[9] M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” in Proc. Eur. Conf. Comput. Vis., 1996, pp. 343–356.
[10] Z. Kalal, J. Matas, and K. Mikolajczyk, “P-N learning: Bootstrapping binary classifiers by structural constraints,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2010, pp. 49–56.
[11] A. Srivastava and E. Klassen, “Bayesian and geometric subspace tracking,” Adv. Appl. Probab., vol. 36, pp. 43–56, Dec. 2004.
[12] A. D. Jepson, D. Fleet, and T. El-Maraghi, “Learning Adaptive Metric for Robust visual tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 10, pp. 1296–1311, Oct. 2003.
[13] N. Alt, S. Hinterstoisser, and N. Navab, “Rapid selection of reliable templates for visual tracking,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1355–1362.
[14] X. Mei, H. Ling, and Y. Wu,“Minimum error bounded efficient l1 tracker with occlusion detection,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2011, pp. 1257–1264.
[15] K.-C. Lee, J. Ho, M.-H. Yang, and D. Kriegman, “Visual tracking and recognition using probabilistic appearance manifolds,” Comput. Vis. Image Understand., vol. 99, no. 3, pp. 303–331, 2005.
[16] M. Andriluka, S. Roth, and B. Schiele, “People-tracking-by-detection and people-detection-by-tracking,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2008, pp. 1–8.
[17] S. Zhou, R. Chellappa, and B. Moghaddam, “Visual tracking and recognition using appearance-adaptive models in particle filters,” IEEE Trans. Image Process., vol. 13, no. 11, pp. 1434–1456, Nov. 2004.

Keywords
Object tracking, Support Vector Machines, Background separation, Gabor filter, Object recognition.