This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Mean Shift Trackers with Cross-Bin Metrics
April 2012 (vol. 34 no. 4)
pp. 695-706
Ido Leichter, Microsoft Israel R&D Center, MSR Adv. Technol. Labs. Israel, Haifa, Israel
Cross-bin metrics have been shown to be more suitable than bin-by-bin metrics for measuring the distance between histograms in various applications. In particular, a visual tracker that minimizes the earth mover's distance (EMD) between the candidate and reference feature histograms has recently been proposed. This tracker was shown to be more robust than the Mean Shift tracker, which employs a bin-by-bin metric. In each frame, the former tracker iteratively shifts the candidate location by one pixel in the direction opposite to the EMD's gradient until no improvement is made. This optimization process involves the clustering of the candidate feature density in feature space, as well as the computation of the EMD between the candidate and reference feature histograms after each shift of the candidate location. In this paper, alternative trackers that employ cross-bin metrics as well, but that are based on Mean Shift (MS) iterations, are derived. The proposed trackers are simpler and faster due to 1) the use of MS-based optimization, which is not restricted to single pixel shifts, 2) abstention from any clustering of feature densities, and 3) abstention from EMD computations in multidimensional spaces.

[1] G. Bradski, "Computer Vision Face Tracking for Use in a Perceptual User Interface," Intel Technology J., Q2, 1998.
[2] R.T. Collins, Y. Liu, and M. Leordeanu, "Online Selection of Discriminative Tracking Features," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, pp. 1631-1643, 2005.
[3] A. Yilmaz, K. Shafique, and M. Shah, "Target Tracking in Airborne Forward Looking Infrared Imagery," Image and Vision Computing, vol. 21, pp. 623-635, 2003.
[4] G. Bieszczad and T. Sosnowski, "Real-Time Mean-Shift Based Tracker for Thermal Vision Systems," Proc. Ninth Int'l Conf. Quantitative InfraRed Thermography, 2008.
[5] A. Adam, E. Rivlin, and I. Shimshoni, "Robust Fragments-Based Tracking Using the Integral Histogram," Proc. 2006 IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 798-805, 2006.
[6] M. Hunke and A. Waibel, "Face Locating and Tracking for Human-Computer Interaction," Proc. 28th Asilomar Conf. Signals, Systems, and Computers, vol. 2, pp. 1277-1281, 1994.
[7] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-577, 2003.
[8] S. McKenna, S. Gong, and Y. Raja, "Face Recognition in Dynamic Scenes," Proc. 1997 British Machine Vision Conf., pp. 140-151, 1997.
[9] S. McKenna, Y. Raja, and S. Gong, "Tracking Colour Objects Using Adaptive Mixture Models," Image and Vision Computing, vol. 17, pp. 225-231, 1999.
[10] S. Birchfield, "Elliptical Head Tracking Using Intensity Gradients and Color Histograms," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 232-237, 1998.
[11] N.S. Peng, J. Yang, and Z. Liu, "Mean Shift Blob Tracking with Kernel Histogram Filtering and Hypothesis Testing," Pattern Recognition Letters, vol. 26, pp. 605-614, 2005.
[12] G.D. Hager, M. Dewan, and C.V. Stewart, "Multiple Kernel Tracking with SSD," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 790-797, 2004.
[13] Y. Rubner, C. Tomasi, and L.J. Guibas, "The Earth Mover's Distance as a Metric for Image Retrieval," Int'l J. Computer Vision, vol. 40, pp. 99-121, 2000.
[14] Q. Zhao, S. Brennan, and H. Tao, "Differential EMD Tracking," Proc. 11th IEEE Int'l Conf. Computer Vision, 2007.
[15] Q. Zhao, Z. Yang, and H. Tao, "Differential Earth Mover's Distance with Its Applications to Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, pp. 274-287, 2010.
[16] F. Bajramovic, B. Deutsch, and C.H. Gräßl, and J. Denzler, "Efficient Adaptive Combination of Histograms for Real-Time Tracking," EURASIP J. Image and Video Processing, Article ID 528297, 2008.
[17] D. Comaniciu, V. Ramesh, and P. Meer, "Real-Time Tracking of Non-Rigid Objects Using Mean Shift," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 142-149, 2000.
[18] D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach toward Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, pp. 603-619, 2002.
[19] R.T. Collins, "Mean-Shift Blob Tracking through Scale Space," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 234-240, 2003.
[20] Z. Fan, Y. Wu, and M. Yang, "Multiple collaborative kernel tracking," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 502-509, 2005.
[21] J. Tu, H. Tao, and T. Huang, "Online Updating Appearance Generative Mixture Model for Meanshift Tracking," Machine Vision and Applications, vol. 20, pp. 163-173, 2009.
[22] A. Elgammal, R. Duraiswami, and L.S. Davis, "Probabilistic Tracking in Joint Feature-Spatial Spaces," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 781-788, 2003.
[23] C. Yang, R. Duraiswami, and L.S. Davis, "Efficient Mean-Shift Tracking via a New Similarity Measure," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 176-183, 2005.
[24] H. Zhang, W. Huang, Z. Huang, and L. Li, "Affine Object Tracking with Kernel-Based Spatial-Color Representation," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 293-300, 2005.
[25] M. Isard and A. Blake, "Condensation—Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, pp. 5-28, 1998.
[26] P. Pérez, C. Hue, J. Vermaak, and M. Gangnet, "Color-Based Probabilistic Tracking," Proc. Seventh European Conf. Computer Vision-Part-I, vol. 1, pp. 661-675, 2002.
[27] C. Shan, T. Tan, and Y. Wei, "Real-Time Hand Tracking Using a Mean Shift Embedded Particle Filter," Pattern Recognition, vol. 40, pp. 1958-1970, 2007.
[28] M. Werman, S. Peleg, and A. Rosenfeld, "A Distance Metric for Multidimensional Histograms," Computer Vision, Graphics, and Image Processing, vol. 32, pp. 328-336, 1985.
[29] R.T. Collins and W. Ge, "CSDD Features: Center-Surround Distribution Distance for Feature Extraction and Matching," Proc. European Conf. Computer Vision, vol. 3, pp. 140-153, 2008.
[30] D. Freedman, R.J. Radke, T. Zhang, Y. Jeong, D.M. Lovelock, and G.T.Y. Chen, "Model-Based Segmentation of Medical Imagery by Matching Distribution," IEEE Trans. Medical Imaging, vol. 24, pp. 281-292, 2005.
[31] H. Ling and K. Okada, "Diffusion Distance for Histogram Comparison," Proc. 2006 IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 246-253, 2006.
[32] J. Hafner, H.S. Sawhney, W. Equitz, M. Flickner, and W. Niblack, "Efficient Color Histogram Indexing for Quadratic Form Distance Functions," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 729-736, 1995.
[33] http://www.cvg.rdg.ac.ukPETS2009/.
[34] http://www.cse.ohio-state.eduotcbvs-bench /.
[35] http://homepages.inf.ed.ac.uk/rbfCAVIARDATA1 /.
[36] K. Okuma, A. Taleghani, N. de Freitas, J.J. Little, and S.G. Lowe, "A Boosted Particle Filter: Multitarget Detection and Tracking," Proc. European Conf. Computer Vision, vol. 1, pp. 28-39, 2004.

Index Terms:
pattern clustering,iterative methods,object tracking,multidimensional space,mean shift trackers,cross-bin metrics,bin-by-bin metrics,visual tracker,earth mover distance,feature histogram,clustering,feature density,feature space,mean shift iteration,MS-based optimization,pixel shift,feature densities,EMD computation,Histograms,Target tracking,Image color analysis,Visualization,Robustness,earth mover's distance.,Visual tracking,Mean Shift,cross-bin metrics
Citation:
Ido Leichter, "Mean Shift Trackers with Cross-Bin Metrics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 695-706, April 2012, doi:10.1109/TPAMI.2011.167
Usage of this product signifies your acceptance of the Terms of Use.