L. Mundermann, S. Corazza, and T. P. Andriacchi, “Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–6.

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

A. Ladikos, S. Benhimane, and N. Navab, “Efficient visual hull computation for real-time 3D reconstruction using CUDA,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (IEEE, 2008), pp. 1–8.

L. Sigal and M. J. Black, “HumanEva: synchronized video and motion capture dataset for evaluation of articulated human motion,” Tech. Rep. (Brown University, 2006).

J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 126–133.

J.-Y. Bouguet, “Pyramidal implementation of the Lucas-Kanade feature tracker,” Microprocessor Research Labs Tech. Rep. (2000).

G. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 714–720.

S. Lazebnik, E. Boyer, and J. Ponce, “On computing exact visual hull of solids bounded by smooth surfaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2001), Vol. 1, pp. I156–I161.

M. Bray, E. Koller-Meier, and L. V. Gool, “Smart particle filtering for high-dimensional tracking,” Comput. Vis. Image Understanding 106, 116–129 (2007).

[CrossRef]

R. Kehl, M. Bray, and L. V. Gool, “Full body tracking from multiple views using stochastic sampling,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 2, pp. 129–136.

W. Matusik, C. Buehler, and L. McMillan, “Polyhedral visual hulls for real-time rendering,” in Proceedings of the 12th Eurographics Workshop on Rendering Techniques (Springer, 2001), pp. 115–126.

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

R. L. Carceroni and K. N. Kutulakos, “Multi-view scene capture by surfel sampling: from video streams to non-rigid 3D motion, shape and reflectance,” Int. J. Comput. Vis. 49, 175–214 (2002).

[CrossRef]

C. Theobalt, J. Carranza, M. Magnor, and H.-P. Seidel, “Combining 3D flow fields with silhouette-based human motion capture for immersive video,” Graph. Models 22, 540–547 (2004).

C. Theobalt, J. Carranza, M. A. Magnor, and H. P. Seidel, “Enhancing silhouette-based human motion capture with 3D motion fields,” in IEEE Pacific Conference on Computer Graphics and Applications (IEEE, 2003), pp. 185–193.

G. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 714–720.

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

L. Mundermann, S. Corazza, and T. P. Andriacchi, “Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–6.

I. Mikic, M. Trivedi, E. Hunter, and P. Cosman, “Human body model acquisition and tracking using voxel data,” Int. J. Comput. Vis. 53, 199–223 (2003).

[CrossRef]

T. Horprasert, D. Harwood, and L. S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” in IEEE International Conference on Computer Vision (IEEE, 1999), pp. 1–19.

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 126–133.

J. Marzat, Y. Dumortier, and A. Ducrot, “Real-time dense and accurate parallel optical flow using CUDA,” 7th International Conference WSCG (2009).

J. Marzat, Y. Dumortier, and A. Ducrot, “Real-time dense and accurate parallel optical flow using CUDA,” 7th International Conference WSCG (2009).

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

M. Gong and Y.-H. Yang, “Disparity flow estimation using orthogonal reliability-based dynamic programming,” in IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 70–73.

M. Bray, E. Koller-Meier, and L. V. Gool, “Smart particle filtering for high-dimensional tracking,” Comput. Vis. Image Understanding 106, 116–129 (2007).

[CrossRef]

R. Kehl, M. Bray, and L. V. Gool, “Full body tracking from multiple views using stochastic sampling,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 2, pp. 129–136.

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

T. Horprasert, D. Harwood, and L. S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” in IEEE International Conference on Computer Vision (IEEE, 1999), pp. 1–19.

G. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 714–720.

T. Horprasert, D. Harwood, and L. S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” in IEEE International Conference on Computer Vision (IEEE, 1999), pp. 1–19.

I. Mikic, M. Trivedi, E. Hunter, and P. Cosman, “Human body model acquisition and tracking using voxel data,” Int. J. Comput. Vis. 53, 199–223 (2003).

[CrossRef]

J. MacCormick and M. Isard, “Partitioned sampling, articulated objects, and interface-quality hand tracking,” in European Conference on Computer Vision, Vol. 1843 of Lecture Notes in Computer Science (Springer, 2000), pp. 3–19.

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

G. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 714–720.

C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Discriminative density propagation for 3D human motion estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 1, pp. 390–397.

R. Kehl, M. Bray, and L. V. Gool, “Full body tracking from multiple views using stochastic sampling,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 2, pp. 129–136.

M. Bray, E. Koller-Meier, and L. V. Gool, “Smart particle filtering for high-dimensional tracking,” Comput. Vis. Image Understanding 106, 116–129 (2007).

[CrossRef]

R. L. Carceroni and K. N. Kutulakos, “Multi-view scene capture by surfel sampling: from video streams to non-rigid 3D motion, shape and reflectance,” Int. J. Comput. Vis. 49, 175–214 (2002).

[CrossRef]

A. Ladikos, S. Benhimane, and N. Navab, “Efficient visual hull computation for real-time 3D reconstruction using CUDA,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (IEEE, 2008), pp. 1–8.

A. Laurentini, “The visual hull: a new tool for contour-based image understanding,” in 7th Scandinavian Conference on Image Analysis (Springer, 1991), pp. 993–1002.

S. Lazebnik, E. Boyer, and J. Ponce, “On computing exact visual hull of solids bounded by smooth surfaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2001), Vol. 1, pp. I156–I161.

K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Q. Appl. Math. 2, 164–168(1994).

C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Discriminative density propagation for 3D human motion estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 1, pp. 390–397.

J. MacCormick and M. Isard, “Partitioned sampling, articulated objects, and interface-quality hand tracking,” in European Conference on Computer Vision, Vol. 1843 of Lecture Notes in Computer Science (Springer, 2000), pp. 3–19.

C. Theobalt, J. Carranza, M. Magnor, and H.-P. Seidel, “Combining 3D flow fields with silhouette-based human motion capture for immersive video,” Graph. Models 22, 540–547 (2004).

C. Theobalt, J. Carranza, M. A. Magnor, and H. P. Seidel, “Enhancing silhouette-based human motion capture with 3D motion fields,” in IEEE Pacific Conference on Computer Graphics and Applications (IEEE, 2003), pp. 185–193.

J. Marzat, Y. Dumortier, and A. Ducrot, “Real-time dense and accurate parallel optical flow using CUDA,” 7th International Conference WSCG (2009).

W. Matusik, C. Buehler, and L. McMillan, “Polyhedral visual hulls for real-time rendering,” in Proceedings of the 12th Eurographics Workshop on Rendering Techniques (Springer, 2001), pp. 115–126.

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

W. Matusik, C. Buehler, and L. McMillan, “Polyhedral visual hulls for real-time rendering,” in Proceedings of the 12th Eurographics Workshop on Rendering Techniques (Springer, 2001), pp. 115–126.

C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Discriminative density propagation for 3D human motion estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 1, pp. 390–397.

I. Mikic, M. Trivedi, E. Hunter, and P. Cosman, “Human body model acquisition and tracking using voxel data,” Int. J. Comput. Vis. 53, 199–223 (2003).

[CrossRef]

L. Mundermann, S. Corazza, and T. P. Andriacchi, “Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–6.

A. Ladikos, S. Benhimane, and N. Navab, “Efficient visual hull computation for real-time 3D reconstruction using CUDA,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (IEEE, 2008), pp. 1–8.

S. Lazebnik, E. Boyer, and J. Ponce, “On computing exact visual hull of solids bounded by smooth surfaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2001), Vol. 1, pp. I156–I161.

Z. Zhang, H. S. Seah, C. K. Quah, and J. Sun, “A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 525–528.

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 126–133.

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

Z. Zhang, H. S. Seah, C. K. Quah, and J. Sun, “A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 525–528.

Z. Zhang, H. S. Seah, and J. Sun, “A hybrid particle swarm optimization with cooperative method for multi-object tracking,” presented at the IEEE Congress on Evolutionary Computing, Brisbane, Australia, 10–15June2012.

C. Theobalt, J. Carranza, M. A. Magnor, and H. P. Seidel, “Enhancing silhouette-based human motion capture with 3D motion fields,” in IEEE Pacific Conference on Computer Graphics and Applications (IEEE, 2003), pp. 185–193.

C. Theobalt, J. Carranza, M. Magnor, and H.-P. Seidel, “Combining 3D flow fields with silhouette-based human motion capture for immersive video,” Graph. Models 22, 540–547 (2004).

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

L. Sigal and M. J. Black, “HumanEva: synchronized video and motion capture dataset for evaluation of articulated human motion,” Tech. Rep. (Brown University, 2006).

C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Discriminative density propagation for 3D human motion estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 1, pp. 390–397.

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

Z. Zhang, H. S. Seah, C. K. Quah, and J. Sun, “A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 525–528.

Z. Zhang, H. S. Seah, and J. Sun, “A hybrid particle swarm optimization with cooperative method for multi-object tracking,” presented at the IEEE Congress on Evolutionary Computing, Brisbane, Australia, 10–15June2012.

R. Szeliski, “Rapid octree construction from image sequences,” CVGIP Image Understanding 58, 23–32 (1993).

[CrossRef]

C. Theobalt, J. Carranza, M. Magnor, and H.-P. Seidel, “Combining 3D flow fields with silhouette-based human motion capture for immersive video,” Graph. Models 22, 540–547 (2004).

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

C. Theobalt, J. Carranza, M. A. Magnor, and H. P. Seidel, “Enhancing silhouette-based human motion capture with 3D motion fields,” in IEEE Pacific Conference on Computer Graphics and Applications (IEEE, 2003), pp. 185–193.

I. Mikic, M. Trivedi, E. Hunter, and P. Cosman, “Human body model acquisition and tracking using voxel data,” Int. J. Comput. Vis. 53, 199–223 (2003).

[CrossRef]

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

M. Gong and Y.-H. Yang, “Disparity flow estimation using orthogonal reliability-based dynamic programming,” in IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 70–73.

Z. Zhang, H. S. Seah, and J. Sun, “A hybrid particle swarm optimization with cooperative method for multi-object tracking,” presented at the IEEE Congress on Evolutionary Computing, Brisbane, Australia, 10–15June2012.

Z. Zhang, H. S. Seah, C. K. Quah, and J. Sun, “A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 525–528.

M. Bray, E. Koller-Meier, and L. V. Gool, “Smart particle filtering for high-dimensional tracking,” Comput. Vis. Image Understanding 106, 116–129 (2007).

[CrossRef]

R. Szeliski, “Rapid octree construction from image sequences,” CVGIP Image Understanding 58, 23–32 (1993).

[CrossRef]

C. Theobalt, J. Carranza, M. Magnor, and H.-P. Seidel, “Combining 3D flow fields with silhouette-based human motion capture for immersive video,” Graph. Models 22, 540–547 (2004).

S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade, “Three-dimensional scene flow,” IEEE Trans. Pattern Anal. Machine Intell. 27, 475–480 (2005).

[CrossRef]

I. Mikic, M. Trivedi, E. Hunter, and P. Cosman, “Human body model acquisition and tracking using voxel data,” Int. J. Comput. Vis. 53, 199–223 (2003).

[CrossRef]

R. L. Carceroni and K. N. Kutulakos, “Multi-view scene capture by surfel sampling: from video streams to non-rigid 3D motion, shape and reflectance,” Int. J. Comput. Vis. 49, 175–214 (2002).

[CrossRef]

K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Q. Appl. Math. 2, 164–168(1994).

Z. Zhang, H. S. Seah, and J. Sun, “A hybrid particle swarm optimization with cooperative method for multi-object tracking,” presented at the IEEE Congress on Evolutionary Computing, Brisbane, Australia, 10–15June2012.

NVIDIA, NVIDIA CUDA programming guide, http://www.nvidia.com/object/cudahomenew.html .

J. Marzat, Y. Dumortier, and A. Ducrot, “Real-time dense and accurate parallel optical flow using CUDA,” 7th International Conference WSCG (2009).

L. Sigal and M. J. Black, “HumanEva: synchronized video and motion capture dataset for evaluation of articulated human motion,” Tech. Rep. (Brown University, 2006).

T. Horprasert, D. Harwood, and L. S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” in IEEE International Conference on Computer Vision (IEEE, 1999), pp. 1–19.

R. Kehl, M. Bray, and L. V. Gool, “Full body tracking from multiple views using stochastic sampling,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 2, pp. 129–136.

L. Mundermann, S. Corazza, and T. P. Andriacchi, “Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–6.

J. Gall, C. Stoll, E. de Aguiar, C. Theobalt, B. Rosenhahn, and H.-P. Seidel, “Motion capture using joint skeleton tracking and surface estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 1746–1753.

J. MacCormick and M. Isard, “Partitioned sampling, articulated objects, and interface-quality hand tracking,” in European Conference on Computer Vision, Vol. 1843 of Lecture Notes in Computer Science (Springer, 2000), pp. 3–19.

C. Sminchisescu, A. Kanaujia, Z. Li, and D. Metaxas, “Discriminative density propagation for 3D human motion estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), Vol. 1, pp. 390–397.

Z. Zhang, H. S. Seah, C. K. Quah, and J. Sun, “A markerless motion capture system with automatic subject-specific body model acquisition and robust pose tracking from 3D data,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 525–528.

A. Laurentini, “The visual hull: a new tool for contour-based image understanding,” in 7th Scandinavian Conference on Image Analysis (Springer, 1991), pp. 993–1002.

J. Deutscher, A. Blake, and I. Reid, “Articulated body motion capture by annealed particle filtering,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 126–133.

C. Theobalt, J. Carranza, M. A. Magnor, and H. P. Seidel, “Enhancing silhouette-based human motion capture with 3D motion fields,” in IEEE Pacific Conference on Computer Graphics and Applications (IEEE, 2003), pp. 185–193.

M. Gong and Y.-H. Yang, “Disparity flow estimation using orthogonal reliability-based dynamic programming,” in IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 70–73.

J.-Y. Bouguet, “Pyramidal implementation of the Lucas-Kanade feature tracker,” Microprocessor Research Labs Tech. Rep. (2000).

G. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler, “A real time system for robust 3D voxel reconstruction of human motions,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), Vol. 2, pp. 714–720.

W. Matusik, C. Buehler, R. Raskar, S. Gortler, and L. McMillan, “Image-based visual hulls,” in Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (ACM, 2000), pp. 369–374.

W. Matusik, C. Buehler, and L. McMillan, “Polyhedral visual hulls for real-time rendering,” in Proceedings of the 12th Eurographics Workshop on Rendering Techniques (Springer, 2001), pp. 115–126.

S. Lazebnik, E. Boyer, and J. Ponce, “On computing exact visual hull of solids bounded by smooth surfaces,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2001), Vol. 1, pp. I156–I161.

A. Ladikos, S. Benhimane, and N. Navab, “Efficient visual hull computation for real-time 3D reconstruction using CUDA,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (IEEE, 2008), pp. 1–8.