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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061745Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1544
 A. Yilmaz, O. Javed, and M. Shah, "Object Tracking: A survey," ACM Computing Surveys, vol. 38, no. 4, pp. 13, 2006.
 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.
 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.
 J. Shi and C. Tomasi, "Good Features to Track," Proc. IEEE Computer Vision and Pattern Recognition, pp. 593-600, 1994.
 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.
 D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, 1999, pp. 91-110, November 2004.
 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.
 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.
 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.
 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.
 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.
 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.
 R. T. Collins, "Mean-shift Blob Tracking through Scale Space," Proc. IEEE Computer Vision and Pattern Recognition, vol. 2, pp. 234-240, 2003.
 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.
 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.
 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.