An Accelerated Human Motion Tracking System Based on Voxel Reconstruction under Complex Environments

In this paper, we propose an automated and markless human motion tracking system, including voxel acquisition and motion tracking. We first explore the problem of voxel reconstruction under a complex environment. Specifically, the procedure of the voxel acquisition is conducted under cluttered background, which makes the high quality silhouette unavailable. An accelerated Bayesian sensor fusion framework combining the information of pixel and super-pixel is adopted to calculate the probability of voxel occupancy, which is achieved by focusing the computation on the image region of interest. The evaluation of reconstruction result is given as well. After the acquisition of voxels, we adopt a hierarchical optimization strategy to solve the problem of human motion tracking in a high-dimensional space. Finally, the performance of our human motion tracking system is compared with the ground truth from a commercial marker motion capture. The experimental results show the proposed human motion tracking system works well under a complex environment.

[1]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.

[2]  Maja J. Mataric,et al.  Markerless kinematic model and motion capture from volume sequences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Toby Howard,et al.  Real-time markerless human body tracking with multi-view 3-d voxel reconstruction. , 2004 .

[4]  Kiriakos N. Kutulakos,et al.  A Theory of Shape by Space Carving , 2000, International Journal of Computer Vision.

[5]  Roberto Cipolla,et al.  Real-Time Tracking of Multiple Articulated Structures in Multiple Views , 2000, ECCV.

[6]  Roberto Cipolla,et al.  Multi-view stereo via volumetric graph-cuts , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Mohan M. Trivedi,et al.  Human Body Model Acquisition and Tracking Using Voxel Data , 2003, International Journal of Computer Vision.

[8]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[10]  Edmond Boyer,et al.  Fusion of multiview silhouette cues using a space occupancy grid , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Anil K. Jain,et al.  On-line fingerprint verification , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[12]  Michael J. Black,et al.  A Quantitative Evaluation of Video-based 3D Person Tracking , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[13]  Roberto Cipolla,et al.  Real-time tracking of highly articulated structures in the presence of noisy measurements , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Richard Szeliski,et al.  Rapid octree construction from image sequences , 1993 .

[16]  Olivier D. Faugeras,et al.  3D Articulated Models and Multiview Tracking with Physical Forces , 2001, Comput. Vis. Image Underst..

[17]  Bruno Raffin,et al.  3D Skeleton-Based Body Pose Recovery , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[18]  Mads Nielsen,et al.  Computer Vision — ECCV 2002 , 2002, Lecture Notes in Computer Science.

[19]  Gang Hua,et al.  Tracking articulated body by dynamic Markov network , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Olivier D. Faugeras,et al.  3D articulated models and multi-view tracking with silhouettes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Qiang Chen,et al.  Robust 3D modeling from silhouette cues , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  R. Zabih,et al.  Exact voxel occupancy with graph cuts , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[24]  Ian D. Reid,et al.  Automatic partitioning of high dimensional search spaces associated with articulated body motion capture , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[25]  Ramakant Nevatia,et al.  Bayesian human segmentation in crowded situations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[26]  Xu Zhao,et al.  3d Human Body Model Initialization Using Single Frame and Tracking Based On Probability Evolutionary Algorithm , 2008 .

[27]  Yuncai Liu,et al.  Probability Evolutionary Algorithm based human motion tracking using voxel data , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[28]  Takeo Kanade,et al.  A real time system for robust 3D voxel reconstruction of human motions , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[29]  L. Davis,et al.  el-based tracking of humans in action: , 1996 .