Abstract
3D human pose estimation is a challenging but important research topic with abundant applications. As for discriminative human pose estimation, the main goal is to learn a nonlinear mapping from image descriptors to 3D human pose configurations, which is difficult due to the high-dimensionality of human pose space and the multimodality of the distribution. To address these problems, we propose a novel motionlet LLC coding on a discriminative framework. A motionlet consists of training examples covering a local area in terms of image space, pose space and time stream. We first group most informative and helpful training examples into motionlets, then perform LLC Coding to learn the nonlinear mapping and get candidate poses, and finally choose the most appropriate pose as the result estimate. To further eliminate ambiguities and improve robustness, we extend our framework to incorporate multiviews. We conduct qualitative evaluation on our Taichi data set and quantitative evaluation on HumanEva data set, which show that our approach has gained the-state-of-the-art performance and significant improvement against previous approaches.
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References
Agarwal, A., Triggs, B.: A Local Basis Representation for Estimating Human Pose from Cluttered Images. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006, Part I. LNCS, vol. 3851, pp. 50–59. Springer, Heidelberg (2006)
Bo, L., Sminchisescu, C.: Twin gaussian processes for structured prediction. IJCV (2010)
Elgammal, A., Lee, C.-S.: Nonlinear manifold learning for dynamic shape and dynamic appearance. CVIU 106(1), 31–46 (2007)
Fergie, M., Galata, A.: Local Gaussian processes for pose recognition from noisy inputs. In: BMVC (2010)
Grauman, K., Shakhnarovich, G., Darell, T.: Inferring 3D structure with a statistical image-based shape model. In: ICCV (2003)
Howe, N.R.: Silhouette lookup for monocular 3D pose tracking. Image and Vision Computing 25(3), 331–341 (2007)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained Linear Coding for image classification. In: CVPR (2010)
Kanaujia, A., Sminchisescu, C., Metaxas, D.: Semi-supervised hierarchical models for 3D human pose reconstruction. In: CVPR (2007)
Ning, H., Wei, X., Gong, Y., Huang, T.: Discriminative learning of visual words for 3D human pose estimation. In: CVPR (2008)
Ong, E.-J., Micilotta, A.S., Bowden, R., Hilton, A.: Viewpoint invariant exemplar-based 3D human tracking. CVIU 104(23), 178–189 (2006)
Poppe, R.W.: Evaluating example-based pose estimation: Experiments on the Humaneva sets. Tech. Report TR-CTIT-07-72, University of Twente (2007)
Rosales, R., Sclaroff, S.: Learning body pose via specialized maps. In: NIPS (2002)
Serre, T., Wolf, L., Poggion, T.: Object recognition with features inspired by visual cortex. In: CVPR (2005)
Shakhnarovich, G., Viola, P.A., Darrel, T.: Fast pose estimation with parameter-sensitive hashing. In: ICCV (2003)
Sigal, L., Black, M.: Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion. Tech. Report CS-06-08, Brown University (2006)
HumanEva project, http://vision.cs.brown.edu/humaneva/
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Discriminative density propagation for 3D human motion estimation. In: CVPR (2005)
Sminchisescu, C., Kanaujia, A., Metaxas, D.: Learning joint top-down and bottom-up processes for 3D visual inference. In: CVPR (2006)
Urtasun, R., Darrel, T.: Local probabilistic regression for activity-indenpendent human pose inference. In: CVPR (2008)
Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: NIPS (2009)
Zhao, X., Ning, H., Liu, Y., Huang, T.: Discriminative estimation of 3D human pose using Gaussian processes. In: CVPR (2008)
Zhao, X., Fu, Y., Liu, Y.: Temporal-Spatial Local Gaussian Process Experts for Human Pose Estimation. In: Zha, H., Taniguchi, R.-I., Maybank, S. (eds.) ACCV 2009, Part I. LNCS, vol. 5994, pp. 364–373. Springer, Heidelberg (2010)
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Sun, L., Song, M., Bu, J., Chen, C. (2012). Pose Estimation with Motionlet LLC Coding. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_40
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DOI: https://doi.org/10.1007/978-3-642-34778-8_40
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