1. A Multiple Camera with Real-Time Volume Reconstruction for Articulated Skeleton Pose Tracking 指導教授：王聖智 教授 學生：謝佳峻 Zheng Zhang, Hock Soon Seah1 Chee Kwang Quah,Alex Ong, and Khalid Jabbar K.-T. Lee et al. (Eds.): MMM 2011, Part I, LNCS 6523, pp. 182–192, 2011.Springer-Verlag Berlin Heidelberg 2011

2. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

3. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

4. Introduction Markerless don’t need markers or special suits. Multi-view deal better with occlusion and appearance ambiguity problems. 建立場景資 訊 剪出主要物 件 還原個體輪廓 形狀 偵測動作與 行為

5. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

6. Multi-camera System System Setup 1.Cameras work synchronously for acquiring multiple image in time. 2. The frame rate of image acquisition should be at least 15 fps. 3. The bandwidth is sufficient for supporting the transfer of multi- video streams. 4.The acquisition room ought to be large. Only one PC !!

7. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

8. Volume Reconstruction Background Subtraction : 目前影像 : 參考背景 : 為一門檻值 1.Background modeling constructs a reference image representing the background. 2.Threshold selection determines appropriate threshold values used in the subtraction operation. 3.Subtraction operation or pixel classication classies the type of a given pixel, i.e., the pixel is the part of background, or it is a moving object.

9. Volume Reconstruction Shape-from-Silhouette and Visual Hulls 1.Each multi-view silhouette contour is firstly obtained. 2.Silhouette polygons are back- projected into their corresponding camera positions. 3. Volume reconstruction method 4.Testing each voxel’s 6- connected neighbors.

10. voxel texture (a)(b)(c) Illustration of volume reconstruction rendered in point clouds (a), voxels without texturing (b) and voxels with texturing (c)

11. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

12. Skeleton Pose Estimation The body model 1. Barrel model 2. 10 body segments (1)(2) 29 DOFs

13. Skeleton Pose Estimation PSO(particle swarm optimization) is the position of the i-th particle at k-th iteration. is the velocity of the i-th particle at k-th iteration. represents a vector of random numbers uniformly distributed in is the history best position found by the i-th particle. is the global best position found by its neighborhood so far. is a constriction coefficient.

14. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

15. Results

16. Results

17. Outline Introduction Multi-camera System Volume Reconstruction Skeleton Pose Estimation Results Conclusion

18. Conclusion 1.Real-time volume sequences are reconstructed for articulated pose recovery. 2.Relies on single PC. 3.Different body segments are not allowed to intersect in the space. 4.Different model points should avoid taking the same closest feature point. Future work will concentrate on enhancing the tracking robustness and accurateness.

19. References Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. Laurentini, A.: The visual hull concept for silhouette-based image understanding. Matusik, W., Buehler, C., McMillan, L.: Polyhedral visual hulls for real-time rendering. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. http://www.csie.ntu.edu.tw/~cyy/courses/vfx/05spring/lectures/scribe/12scr ibe.pdf