J. Jeyakar, R. V. Babu, and K. R. Ramakrishnan, “Robust object tracking with background-weighted local kernels,” Comput. Vision Image Underst. 112, 296-309 (2008).

[CrossRef]

J. S. Hu, C. W. Juan, and J. J Wang, “A spatial-color mean-shift object tracking algorithm with scale and orientation estimation,” Pattern Recogn. Lett. 29, 2165-2173 (2008).

[CrossRef]

R. V. Babu, P. Perez, and P. Bouthemy, “Robust tracking with motion estimation and local kernel-based color modeling,” Image Vision Comput. 25, 1205-1216 (2007).

[CrossRef]

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

J. G. Ling, E. Q. Liu, H. Y. Liang, and J. Yang, “Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure,” Appl. Opt. 46, 3239-3252(2007).

[CrossRef]
[PubMed]

C. F. Shana, T. N. Tan, and Y. C. Wei, “Real-time hand tracking using a mean shift embedded particle filter,” Pattern Recogn. 40, 1958-1970 (2007).

[CrossRef]

R. M. Liu, E. Q. Liu, J. Yang, Y. Zeng, F. L. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga--Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780-7791 (2007).

[CrossRef]
[PubMed]

K. Brunnstrom, B. N. Schenkman, and B. Jacobson, “Object detection in cluttered infrared images,” Opt. Eng. 42, 388-399 (2003).

[CrossRef]

K. Nummiaro, E. Koller-Meier, and L. V. Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99-110(2003).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564-577 (2003).

[CrossRef]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603-619 (2002).

[CrossRef]

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

M. Isard and A. Blake, “CONDENSATION: conditional density propagation for visual tracking,” Int. J. Comput. Vision 29, 5-28 (1998).

[CrossRef]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

F. Aherne, N. Thacker, and P. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika 34, 363-368 (1997).

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 790-799 (1995).

[CrossRef]

K. Fukunaga and L. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Trans. Inf. Theory 21, 32-40 (1975).

[CrossRef]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

F. Aherne, N. Thacker, and P. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika 34, 363-368 (1997).

M. Yasuno, S. Ryousuke, N. Yasuda, and M. Aoki, “Pedestrian detection and tracking in far infrared images,” in *IEEE Conference on Intelligent Transportation Systems, 2005* (IEEE, 2005), pp. 182-187.

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

J. Jeyakar, R. V. Babu, and K. R. Ramakrishnan, “Robust object tracking with background-weighted local kernels,” Comput. Vision Image Underst. 112, 296-309 (2008).

[CrossRef]

R. V. Babu, P. Perez, and P. Bouthemy, “Robust tracking with motion estimation and local kernel-based color modeling,” Image Vision Comput. 25, 1205-1216 (2007).

[CrossRef]

M. Isard and A. Blake, “CONDENSATION: conditional density propagation for visual tracking,” Int. J. Comput. Vision 29, 5-28 (1998).

[CrossRef]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

R. V. Babu, P. Perez, and P. Bouthemy, “Robust tracking with motion estimation and local kernel-based color modeling,” Image Vision Comput. 25, 1205-1216 (2007).

[CrossRef]

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

K. Brunnstrom, B. N. Schenkman, and B. Jacobson, “Object detection in cluttered infrared images,” Opt. Eng. 42, 388-399 (2003).

[CrossRef]

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 790-799 (1995).

[CrossRef]

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564-577 (2003).

[CrossRef]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603-619 (2002).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Real-time tracking of non-rigid objects using mean shift,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2000* (IEEE, 2000), pp. 142-149.

D. Comaniciu and V. Ramesh, “Mean shift and optimal prediction for efficient object tracking,” in *IEEE Conference on Image Processing, 2000* (IEEE, 2000), pp. 70-73.

J. W. Davis, “OTCBVS benchmark dataset collection,” http://www.cse.ohio-state.edu/otcbvs-bench/.

C. Yang, R. Duraiswami, and L. Davis, “Efficient mean-shift tracking via a new similarity measure,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2005* (IEEE, 2005), pp. 176-183.

C. Yang, R. Duraiswami, and L. Davis, “Efficient mean-shift tracking via a new similarity measure,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2005* (IEEE, 2005), pp. 176-183.

K. Fukunaga and L. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Trans. Inf. Theory 21, 32-40 (1975).

[CrossRef]

K. Nummiaro, E. Koller-Meier, and L. V. Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99-110(2003).

[CrossRef]

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

K. Fukunaga and L. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Trans. Inf. Theory 21, 32-40 (1975).

[CrossRef]

J. S. Hu, C. W. Juan, and J. J Wang, “A spatial-color mean-shift object tracking algorithm with scale and orientation estimation,” Pattern Recogn. Lett. 29, 2165-2173 (2008).

[CrossRef]

M. Isard and A. Blake, “CONDENSATION: conditional density propagation for visual tracking,” Int. J. Comput. Vision 29, 5-28 (1998).

[CrossRef]

K. Brunnstrom, B. N. Schenkman, and B. Jacobson, “Object detection in cluttered infrared images,” Opt. Eng. 42, 388-399 (2003).

[CrossRef]

J. Jeyakar, R. V. Babu, and K. R. Ramakrishnan, “Robust object tracking with background-weighted local kernels,” Comput. Vision Image Underst. 112, 296-309 (2008).

[CrossRef]

J. S. Hu, C. W. Juan, and J. J Wang, “A spatial-color mean-shift object tracking algorithm with scale and orientation estimation,” Pattern Recogn. Lett. 29, 2165-2173 (2008).

[CrossRef]

K. Nummiaro, E. Koller-Meier, and L. V. Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99-110(2003).

[CrossRef]

J. G. Ling, E. Q. Liu, H. Y. Liang, and J. Yang, “Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure,” Appl. Opt. 46, 3239-3252(2007).

[CrossRef]
[PubMed]

R. M. Liu, E. Q. Liu, J. Yang, Y. Zeng, F. L. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga--Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780-7791 (2007).

[CrossRef]
[PubMed]

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564-577 (2003).

[CrossRef]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603-619 (2002).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Real-time tracking of non-rigid objects using mean shift,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2000* (IEEE, 2000), pp. 142-149.

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

K. Nummiaro, E. Koller-Meier, and L. V. Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99-110(2003).

[CrossRef]

R. V. Babu, P. Perez, and P. Bouthemy, “Robust tracking with motion estimation and local kernel-based color modeling,” Image Vision Comput. 25, 1205-1216 (2007).

[CrossRef]

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

J. Jeyakar, R. V. Babu, and K. R. Ramakrishnan, “Robust object tracking with background-weighted local kernels,” Comput. Vision Image Underst. 112, 296-309 (2008).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564-577 (2003).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Real-time tracking of non-rigid objects using mean shift,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2000* (IEEE, 2000), pp. 142-149.

D. Comaniciu and V. Ramesh, “Mean shift and optimal prediction for efficient object tracking,” in *IEEE Conference on Image Processing, 2000* (IEEE, 2000), pp. 70-73.

F. Aherne, N. Thacker, and P. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika 34, 363-368 (1997).

M. Yasuno, S. Ryousuke, N. Yasuda, and M. Aoki, “Pedestrian detection and tracking in far infrared images,” in *IEEE Conference on Intelligent Transportation Systems, 2005* (IEEE, 2005), pp. 182-187.

K. Brunnstrom, B. N. Schenkman, and B. Jacobson, “Object detection in cluttered infrared images,” Opt. Eng. 42, 388-399 (2003).

[CrossRef]

C. F. Shana, T. N. Tan, and Y. C. Wei, “Real-time hand tracking using a mean shift embedded particle filter,” Pattern Recogn. 40, 1958-1970 (2007).

[CrossRef]

C. F. Shana, T. N. Tan, and Y. C. Wei, “Real-time hand tracking using a mean shift embedded particle filter,” Pattern Recogn. 40, 1958-1970 (2007).

[CrossRef]

F. Aherne, N. Thacker, and P. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika 34, 363-368 (1997).

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

J. S. Hu, C. W. Juan, and J. J Wang, “A spatial-color mean-shift object tracking algorithm with scale and orientation estimation,” Pattern Recogn. Lett. 29, 2165-2173 (2008).

[CrossRef]

C. F. Shana, T. N. Tan, and Y. C. Wei, “Real-time hand tracking using a mean shift embedded particle filter,” Pattern Recogn. 40, 1958-1970 (2007).

[CrossRef]

C. Yang, R. Duraiswami, and L. Davis, “Efficient mean-shift tracking via a new similarity measure,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2005* (IEEE, 2005), pp. 176-183.

R. M. Liu, E. Q. Liu, J. Yang, Y. Zeng, F. L. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga--Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780-7791 (2007).

[CrossRef]
[PubMed]

J. G. Ling, E. Q. Liu, H. Y. Liang, and J. Yang, “Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure,” Appl. Opt. 46, 3239-3252(2007).

[CrossRef]
[PubMed]

M. Yasuno, S. Ryousuke, N. Yasuda, and M. Aoki, “Pedestrian detection and tracking in far infrared images,” in *IEEE Conference on Intelligent Transportation Systems, 2005* (IEEE, 2005), pp. 182-187.

M. Yasuno, S. Ryousuke, N. Yasuda, and M. Aoki, “Pedestrian detection and tracking in far infrared images,” in *IEEE Conference on Intelligent Transportation Systems, 2005* (IEEE, 2005), pp. 182-187.

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

A. Bal and M. S. Alam, “Dynamic target tracking with fringe-adjusted joint transform correlation and template matching,” Appl. Opt. 43, 4874-4881 (2004).

[CrossRef]
[PubMed]

R. M. Liu, E. Q. Liu, J. Yang, Y. Zeng, F. L. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga--Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780-7791 (2007).

[CrossRef]
[PubMed]

J. G. Ling, E. Q. Liu, H. Y. Liang, and J. Yang, “Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure,” Appl. Opt. 46, 3239-3252(2007).

[CrossRef]
[PubMed]

J. Jeyakar, R. V. Babu, and K. R. Ramakrishnan, “Robust object tracking with background-weighted local kernels,” Comput. Vision Image Underst. 112, 296-309 (2008).

[CrossRef]

K. Fukunaga and L. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Trans. Inf. Theory 21, 32-40 (1975).

[CrossRef]

Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 790-799 (1995).

[CrossRef]

D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 564-577 (2003).

[CrossRef]

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Mach. Intell. 24, 603-619 (2002).

[CrossRef]

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174-188 (2002).

[CrossRef]

K. Nummiaro, E. Koller-Meier, and L. V. Gool, “An adaptive color-based particle filter,” Image Vision Comput. 21, 99-110(2003).

[CrossRef]

R. V. Babu, P. Perez, and P. Bouthemy, “Robust tracking with motion estimation and local kernel-based color modeling,” Image Vision Comput. 25, 1205-1216 (2007).

[CrossRef]

P. Brasnett, L. Mihaylova, D. Bull, and N. Canagarajah, “Sequential Monte Carlo tracking by fusing multiple cues in video sequences,” Image Vision Comput. 25, 1217-1227(2007).

[CrossRef]

M. Isard and A. Blake, “CONDENSATION: conditional density propagation for visual tracking,” Int. J. Comput. Vision 29, 5-28 (1998).

[CrossRef]

F. Aherne, N. Thacker, and P. Rockett, “The Bhattacharyya metric as an absolute similarity measure for frequency coded data,” Kybernetika 34, 363-368 (1997).

D. Borghys, P. Verlinde, C. Perneel, and M. Acheroy, “Multilevel data fusion for the detection of targets using multispectral image sequences,” Opt. Eng. 37, 477-484 (1998).

[CrossRef]

K. Brunnstrom, B. N. Schenkman, and B. Jacobson, “Object detection in cluttered infrared images,” Opt. Eng. 42, 388-399 (2003).

[CrossRef]

C. F. Shana, T. N. Tan, and Y. C. Wei, “Real-time hand tracking using a mean shift embedded particle filter,” Pattern Recogn. 40, 1958-1970 (2007).

[CrossRef]

J. S. Hu, C. W. Juan, and J. J Wang, “A spatial-color mean-shift object tracking algorithm with scale and orientation estimation,” Pattern Recogn. Lett. 29, 2165-2173 (2008).

[CrossRef]

H. Liu, Z. Yu, H. B. Zha, Y. X. Zou, and L. Zhang, “Robust human tracking based on multi-cue integration and mean-shift,” Pattern Recogn. Lett. (to be published).

[CrossRef]

J. W. Davis, “OTCBVS benchmark dataset collection,” http://www.cse.ohio-state.edu/otcbvs-bench/.

M. Yasuno, S. Ryousuke, N. Yasuda, and M. Aoki, “Pedestrian detection and tracking in far infrared images,” in *IEEE Conference on Intelligent Transportation Systems, 2005* (IEEE, 2005), pp. 182-187.

D. Comaniciu, V. Ramesh, and P. Meer, “Real-time tracking of non-rigid objects using mean shift,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2000* (IEEE, 2000), pp. 142-149.

C. Yang, R. Duraiswami, and L. Davis, “Efficient mean-shift tracking via a new similarity measure,” in *IEEE Conference on Computer Vision and Pattern Recognition, 2005* (IEEE, 2005), pp. 176-183.

D. Comaniciu and V. Ramesh, “Mean shift and optimal prediction for efficient object tracking,” in *IEEE Conference on Image Processing, 2000* (IEEE, 2000), pp. 70-73.