Object Tracking System Using Camshift, Meanshift and Kalman Filter
This paper presents a implementation of an object tracking system in a video sequence. This object tracking is an important task in many vision applications. The main steps in video analysis are two: detection of interesting moving objects and tracking of such objects from frame to frame. In a similar vein, most tracking algorithms use pre-specified methods for preprocessing. In our work, we have implemented several object tracking algorithms (Meanshift, Camshift, Kalman filter) with different preprocessing methods. Then, we have evaluated the performance of these algorithms for different video sequences. The obtained results have shown good performances according to the degree of applicability and evaluation criteria.
[1] A. Alper and J. Omar and S. Mubarak, " Object Tracking: A Survey".
ACM Computing Surveys, vol. 38, no. 45, pp. 1-45. 2006.
[2] D. Chen and F. Bai and B. Li and T. Wang, " BEST: A Real-time
Tracking Method for Scout Robot". IEEE/RSJ International Conference
on Intelligent Robots and Systems, no. 6, pp. 1001-1006. 2009.
[3] J. Wang and F. He and X. Zhang and G. Yun, " Tracking Object throuth
Occuusions Using Improved Kalman Filter". IEEE 2010, no. 6, pp. 223-
228. 2010.
[4] N.A Gmez, " A Probabilistic Integrated Object Recognition and Tracking
Framework for Video Sequences". Universitat Rovira I Virgili, PHD
thesis, Espagne, 2009.
[5] X. Sun and H. Yao, and S. Zhang, " A Refined Particle Filter Method for
Contour Tracking". Visual Communications and Image Processing, no. 8,
pp. 1-8. 2010.
[6] D. Comaniciu, and V. Ramesh, and P. Meer , " Kernel-Based Object
Tracking". IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 25 no. 14, pp. 564-577. 2003.
[7] L. Ido, and L. Michael, and R. Ehud, " Mean Shift tracking with multiple
reference color histograms". Computer Vision and Image Understanding,
no. 9, pp. 400-408. 2010.
[8] G. bradski, and R. Gary, and L.M. Olga, " Computer Vision Face Tracking
For Use in a Perceptual User Interface". Intel Technology Journal Q2-98,
vol. 10, no. 15, pp. 1-15. 1998.
[9] G. Bradski, and T. Ogiuchi, and M. Higashikubo, " Visual Tracking
Algorithm using Pixel-Pair Feature". International Conference on Pattern
Recognition, no. 4, pp. 1808-1811. 2010.
[10] Y. Ruiguo, and Z. Xinrong, " The Design and Implementation of Face
Tracking in Real Time Multimedia Recording System". IEEE Transaction,
no. 3, pp. 1-3. 2009.
[11] E. David, and B. Erich, and K. Daniel, and S. Anselm, " Fast and Robust
Camshift Tracking". IEEE Transaction, no. 8, pp. 1-8. 2010.
[12] S. Avidan, " Support vector tracking". In IEEE Conference on Computer
Vision and Pattern Recognition, no. 8, pp.184-191. 2001.
[13] S. Franois, B. and R.J. Alexandre, " Camshift Tracker Design Experiments".
IMSC, no. 11, pp. 1-11. 2004.
[14] L. Zhang, and D. Zhang, " Robust Object Tracking Using Joint Color
Texture Histogram". International Journal of Pattern Recognition and
Artificial Intelligence, vol. 23 no. 19, pp. 1245-1263. 2009.
[15] X. Song, and R. Nevatia, " Camshift Tracker Design Experiments with
Intel OpenCV and Sai". International Conference on Pattern Recognition,
IEEE Computer Society, no. 4, pp. 1-4. 2004.
[16] S. Afzal, and N. Vaswani, and A. Tannenbaum ,A. and Yezzi, " Object
Tracking: A Survey". IEEE, no. 6, pp. 4244-4249. 2009.
[17] A. Iyad, and H. Mahmoud, " Smart Human Face Detection System".
International Journal of Computers, vol. 5, no. 8, pp. 210-221. 2011.
[18] C. Abbas, and C. Joan, and C. Kevin, and M.K. Paul " A Skin Tone
Detection Algorithm for an Adaptive Approach to Steganography". , no.
23, pp. 1-23. 2011.
[19] http://developer.com/technology/itj.
[20] http://ftp.pets.rdg.ac.uk/PETS2001/DATASET1/TESTING/.
[21] http://developer.com/technology/itj.
[22] http://ftp.pets.rdg.ac.uk/PETS2001/DATASET1/TESTING/.
[1] A. Alper and J. Omar and S. Mubarak, " Object Tracking: A Survey".
ACM Computing Surveys, vol. 38, no. 45, pp. 1-45. 2006.
[2] D. Chen and F. Bai and B. Li and T. Wang, " BEST: A Real-time
Tracking Method for Scout Robot". IEEE/RSJ International Conference
on Intelligent Robots and Systems, no. 6, pp. 1001-1006. 2009.
[3] J. Wang and F. He and X. Zhang and G. Yun, " Tracking Object throuth
Occuusions Using Improved Kalman Filter". IEEE 2010, no. 6, pp. 223-
228. 2010.
[4] N.A Gmez, " A Probabilistic Integrated Object Recognition and Tracking
Framework for Video Sequences". Universitat Rovira I Virgili, PHD
thesis, Espagne, 2009.
[5] X. Sun and H. Yao, and S. Zhang, " A Refined Particle Filter Method for
Contour Tracking". Visual Communications and Image Processing, no. 8,
pp. 1-8. 2010.
[6] D. Comaniciu, and V. Ramesh, and P. Meer , " Kernel-Based Object
Tracking". IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 25 no. 14, pp. 564-577. 2003.
[7] L. Ido, and L. Michael, and R. Ehud, " Mean Shift tracking with multiple
reference color histograms". Computer Vision and Image Understanding,
no. 9, pp. 400-408. 2010.
[8] G. bradski, and R. Gary, and L.M. Olga, " Computer Vision Face Tracking
For Use in a Perceptual User Interface". Intel Technology Journal Q2-98,
vol. 10, no. 15, pp. 1-15. 1998.
[9] G. Bradski, and T. Ogiuchi, and M. Higashikubo, " Visual Tracking
Algorithm using Pixel-Pair Feature". International Conference on Pattern
Recognition, no. 4, pp. 1808-1811. 2010.
[10] Y. Ruiguo, and Z. Xinrong, " The Design and Implementation of Face
Tracking in Real Time Multimedia Recording System". IEEE Transaction,
no. 3, pp. 1-3. 2009.
[11] E. David, and B. Erich, and K. Daniel, and S. Anselm, " Fast and Robust
Camshift Tracking". IEEE Transaction, no. 8, pp. 1-8. 2010.
[12] S. Avidan, " Support vector tracking". In IEEE Conference on Computer
Vision and Pattern Recognition, no. 8, pp.184-191. 2001.
[13] S. Franois, B. and R.J. Alexandre, " Camshift Tracker Design Experiments".
IMSC, no. 11, pp. 1-11. 2004.
[14] L. Zhang, and D. Zhang, " Robust Object Tracking Using Joint Color
Texture Histogram". International Journal of Pattern Recognition and
Artificial Intelligence, vol. 23 no. 19, pp. 1245-1263. 2009.
[15] X. Song, and R. Nevatia, " Camshift Tracker Design Experiments with
Intel OpenCV and Sai". International Conference on Pattern Recognition,
IEEE Computer Society, no. 4, pp. 1-4. 2004.
[16] S. Afzal, and N. Vaswani, and A. Tannenbaum ,A. and Yezzi, " Object
Tracking: A Survey". IEEE, no. 6, pp. 4244-4249. 2009.
[17] A. Iyad, and H. Mahmoud, " Smart Human Face Detection System".
International Journal of Computers, vol. 5, no. 8, pp. 210-221. 2011.
[18] C. Abbas, and C. Joan, and C. Kevin, and M.K. Paul " A Skin Tone
Detection Algorithm for an Adaptive Approach to Steganography". , no.
23, pp. 1-23. 2011.
[19] http://developer.com/technology/itj.
[20] http://ftp.pets.rdg.ac.uk/PETS2001/DATASET1/TESTING/.
[21] http://developer.com/technology/itj.
[22] http://ftp.pets.rdg.ac.uk/PETS2001/DATASET1/TESTING/.
@article{"International Journal of Electrical, Electronic and Communication Sciences:64033", author = "Afef Salhi and Ameni Yengui Jammaoussi", title = "Object Tracking System Using Camshift, Meanshift and Kalman Filter", abstract = "This paper presents a implementation of an object tracking system in a video sequence. This object tracking is an important task in many vision applications. The main steps in video analysis are two: detection of interesting moving objects and tracking of such objects from frame to frame. In a similar vein, most tracking algorithms use pre-specified methods for preprocessing. In our work, we have implemented several object tracking algorithms (Meanshift, Camshift, Kalman filter) with different preprocessing methods. Then, we have evaluated the performance of these algorithms for different video sequences. The obtained results have shown good performances according to the degree of applicability and evaluation criteria.
", keywords = "Tracking, meanshift, camshift, Kalman filter, evaluation.", volume = "6", number = "4", pages = "466-6", }
