Probabilistic Center Voting Method for Subsequent Object Tracking and Segmentation
In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.
[1] A. Yilmaz, O. Javed, and M. Shah, "Object Tracking: A survey," ACM
Computing Surveys, vol. 38, no. 4, pp. 13, 2006.
[2] M. Isard and A. Blake, "CONDENSATION - Conditional Density Propagation
for Visual Tracking," International Journal of Computer Vision,
vol. 29, no. 1, pp. 5-28, August 1998.
[3] Y. Shi and W. C. Karl, "Real-Time Tracking Using Level Sets," Proc.
IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 34-41, June
2005.
[4] J. Shi and C. Tomasi, "Good Features to Track," Proc. IEEE Computer
Vision and Pattern Recognition, pp. 593-600, 1994.
[5] C. Tomasi and T. Kanade, "Detection and Tracking of Point Features,"
Technical Report CMU-CS-91132, Pittsburgh:Carnegie Mellon University
School of Computer Science, April 1991.
[6] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints,"
International Journal of Computer Vision, vol. 60, 1999, pp. 91-110,
November 2004.
[7] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up
Robust Features," Computer Vision and Image Understanding, vol. 110,
no. 3, pp. 346-359, June 2008.
[8] G.R. Bradski, "Real Time Face and Object Tracking as A Component of
A Perceptual User Interface," Applications of Computer Vision, pp. 214
- 219, 1998.
[9] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based Object Tracking,"
IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no.
5, May 2003.
[10] S. T. Birchfield and S. Rangarajan, "Spatiograms versus Histograms
for Region-Based Tracking," Proc. IEEE Computer Vision and Pattern
Recognition, vol. 2, pp. 1158-1163, June 2005.
[11] Q. Zhao and H. Tao, "Object Tracking Using Color Correlogram," Proc.
IEEE Visual Surveillance and Performance Evaluation of Tracking and
Surveillance, pp. 263-270, October 2005.
[12] B.D. Lucas and T. Kanade, "An Iterative Image Registration Technique
with an Application to Stereo Vision," Proc. of the 7th International Joint
Conference on Artificial Intelligence, Vancouver, pp. 674-679, 1981.
[13] R. T. Collins, "Mean-shift Blob Tracking through Scale Space," Proc.
IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 234-240,
2003.
[14] C. Stauffer and W.E.L. Grimson, "Adaptive Background Mixture Models
for Real-Time Tracking," Proc. IEEE Computer Vision and Pattern
Recognition, vol. 2, pp. 246-252, June 1999.
[15] A. Elgammal, D. Harwood, and L.S. Davis, "Non-parametric Model for
Background Subtraction," European Conference on Computer Vision, vol.
2, pp. 751-767, 2000.
[16] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-Time
Foreground Background Segmentation Using Codebook Model," Real-
Time Imaging, vol. 11, no. 3, pp. 172-185, June 2005.
[1] A. Yilmaz, O. Javed, and M. Shah, "Object Tracking: A survey," ACM
Computing Surveys, vol. 38, no. 4, pp. 13, 2006.
[2] M. Isard and A. Blake, "CONDENSATION - Conditional Density Propagation
for Visual Tracking," International Journal of Computer Vision,
vol. 29, no. 1, pp. 5-28, August 1998.
[3] Y. Shi and W. C. Karl, "Real-Time Tracking Using Level Sets," Proc.
IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 34-41, June
2005.
[4] J. Shi and C. Tomasi, "Good Features to Track," Proc. IEEE Computer
Vision and Pattern Recognition, pp. 593-600, 1994.
[5] C. Tomasi and T. Kanade, "Detection and Tracking of Point Features,"
Technical Report CMU-CS-91132, Pittsburgh:Carnegie Mellon University
School of Computer Science, April 1991.
[6] D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints,"
International Journal of Computer Vision, vol. 60, 1999, pp. 91-110,
November 2004.
[7] H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up
Robust Features," Computer Vision and Image Understanding, vol. 110,
no. 3, pp. 346-359, June 2008.
[8] G.R. Bradski, "Real Time Face and Object Tracking as A Component of
A Perceptual User Interface," Applications of Computer Vision, pp. 214
- 219, 1998.
[9] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based Object Tracking,"
IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no.
5, May 2003.
[10] S. T. Birchfield and S. Rangarajan, "Spatiograms versus Histograms
for Region-Based Tracking," Proc. IEEE Computer Vision and Pattern
Recognition, vol. 2, pp. 1158-1163, June 2005.
[11] Q. Zhao and H. Tao, "Object Tracking Using Color Correlogram," Proc.
IEEE Visual Surveillance and Performance Evaluation of Tracking and
Surveillance, pp. 263-270, October 2005.
[12] B.D. Lucas and T. Kanade, "An Iterative Image Registration Technique
with an Application to Stereo Vision," Proc. of the 7th International Joint
Conference on Artificial Intelligence, Vancouver, pp. 674-679, 1981.
[13] R. T. Collins, "Mean-shift Blob Tracking through Scale Space," Proc.
IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 234-240,
2003.
[14] C. Stauffer and W.E.L. Grimson, "Adaptive Background Mixture Models
for Real-Time Tracking," Proc. IEEE Computer Vision and Pattern
Recognition, vol. 2, pp. 246-252, June 1999.
[15] A. Elgammal, D. Harwood, and L.S. Davis, "Non-parametric Model for
Background Subtraction," European Conference on Computer Vision, vol.
2, pp. 751-767, 2000.
[16] K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-Time
Foreground Background Segmentation Using Codebook Model," Real-
Time Imaging, vol. 11, no. 3, pp. 172-185, June 2005.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54907", author = "Suryanto and Hyo-Kak Kim and Sang-Hee Park and Dae-Hwan Kim and Sung-Jea Ko", title = "Probabilistic Center Voting Method for Subsequent Object Tracking and Segmentation", abstract = "In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.
", keywords = "center voting, back projection, object tracking, size adaptation, non-stationary camera tracking.", volume = "3", number = "11", pages = "2033-5", }
