Abstract
We address the problem of how human pose in 3D can be estimated from video data. The use of multiple views has the potential of tackling self-occlusion of the human subject in any particular view, as well as of estimating the human pose more precisely. We propose a scheme of allowing multiple views to be put together naturally for determining human pose, allowing hypotheses of the body parts in each view to be pruned away efficiently through consistency check over all the views. The scheme relates the different views through a linear combination-like expression of all the image data, which captures the rigidity of the human subject in 3D. The scheme does not require thorough calibration of the cameras themselves nor the camera inter-geometry. A formulation is also introduced that expresses the multi-view scheme, as well as other constraints, in the pose estimation problem. A belief propagation approach is used to reach a final human pose under the formulation. Experimental results on in-house captured image data as well as publicly available benchmark datasets are shown to illustrate the performance of the system.
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References
Moeslund, T., Granum, E.: A Survey of Computer Vision-Based Human Motion Capture. Computer vision and Image Understanding 81(3), 231–268 (2001)
Moeslund, T., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer vision and Image Understanding 103, 90–126 (2006)
Lee, M.W., Cohen, I.: Proposal maps driven mcmc for estimating human body pose in static images. In: CVPR, vol. 2, pp. 334–341 (2004)
Hua, G., Yang, M.H., Wu, Y.: Learning to estimate human pose with data driven belief propagation. In: CVPR, vol. 2, pp. 747–754 (2005)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. IJCV 61(1), 55–79 (2005)
Peursum, P., Venkatesh, S., West, G.: A Study on Smoothing for Particle-Filtered 3D Human Body Tracking. IJCV 87(1-2) (2010)
Ramanan, D., Forsyth, D., Zisserman, A.: Tracking People by Learning Their Appearance. TPAMI 29(1), 65–81 (2007)
Andriluka, M., Roth, S., Schiele, B.: Pictorial Structures Revisited: People Detection and Articulated Pose Estimation. In: CVPR (2009)
Ferrari, V., Marin, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: CVPR (2008)
Andriluka, M., Roth, S., Schiele, B.: Monocular 3D Pose Estimation and Tracking by Detection. In: CVPR (2010)
Wang, Z., Chung, R.: Articulated Human Body: 3D Pose Estimation using a Single Camera. In: ICPR (2010)
Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. International Journal of Robotics Research 22(6), 371–391 (2003)
Agarwal, A., Triggs, B.: 3D human pose from silhouettes by relevance vector regression. In: CVPR, pp. 882–888 (2004)
Elgammal, A., Lee, C.: Inferring 3d body pose from silhouettes using activity manifold learning. In: CVPR, pp. 681–688 (2004)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: CVPR (2005)
Bergtholdt, M., Kappes, J., Schmidt, S., Schnörr, C.: A Study of Parts-Based Object Class Detection Using Complete Graphs complete graph. IJCV 87(1-2) (2010)
Bernier, O., Cheung-Mon-Chan, P., Bouguet, A.: Fast nonparametric belief propagation for real-time stereo articulated body tracking. Computer Vision and Image Understanding 113, 29–47 (2009)
Gupta, A., Mittal, A., Davis, L.S.: Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions. TPAMI 30(3) (2008)
Cheung, K.M., Baker, S., Kanade, T.: Shape-from-silhouette of articulated objects and its use for human body kinematics estimation and motion capture. In: CVPR, vol. 1, pp. 77–84 (2003)
Corazza, S., Mundermann, L., Gambaretto, E., Ferrigno, G., Andriacchi, T.: Markerless motion capture through visual hull, articulated ICP and subject specific model generation. IJCV 87(1-2) (2010)
Sigal, L., Black, M.J.: Guest Editorial: State of Art in Image- and Video-Based Human Pose and Motion Estimation. IJCV 87(1-3) (2010)
Sigal, L., Black, M.J.: Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion. In: Techniacl Report CS-06-08, Brown University (2006)
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Wang, Z., Chung, R. (2010). Integrating Multiple Uncalibrated Views for Human 3D Pose Estimation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_29
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DOI: https://doi.org/10.1007/978-3-642-17277-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
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