a multiple camera with real time volume reconstruction for articulated skeleton pose tracking
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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

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a multiple camera with real time volume reconstruction for articulated skeleton pose tracking

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 CheeKwangQuah,AlexOng, and Khalid Jabbar

K.-T. Lee et al. (Eds.): MMM 2011, Part I, LNCS 6523, pp. 182–192, 2011.Springer-VerlagBerlin Heidelberg 2011

outline
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
outline1
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
introduction
Introduction
  • Markerless

don’t need markers or special suits.

  • Multi-view

deal better with occlusion and appearance ambiguity problems.

剪出主要物件

還原個體輪廓形狀

偵測動作與行為

建立場景資訊

outline2
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
multi camera system
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 !!

outline3
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
volume reconstruction
Volume Reconstruction
  • Background Subtraction

Background modeling constructs a reference image representing the background.

Threshold selection determines appropriate threshold values used in the subtraction operation.

Subtraction operation or pixel classicationclassies the type of a given pixel, i.e., the pixel is the part of background, or it is a moving object.

: 目前影像

: 參考背景

: 為一門檻值

volume reconstruction1
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.

slide10

voxel

texture

(a)

(b)

(c)

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

outline4
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
skeleton pose estimation
Skeleton Pose Estimation
  • The body model
  • Barrel model
  • 10 body segments

29 DOFs

(1)

(2)

skeleton pose estimation1
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 .

outline5
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
outline6
Outline
  • Introduction
  • Multi-camera System
  • Volume Reconstruction
  • Skeleton Pose Estimation
  • Results
  • Conclusion
conclusion
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.

references
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/12scribe.pdf
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