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
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
don’t need markers or special suits.
deal better with occlusion and appearance ambiguity problems.
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 !!
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.
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.
Illustration of volume reconstruction rendered in point clouds (a), voxels without texturing (b) and voxels with texturing (c)
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 .
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.