Abstract
The present paper considers the supplement of prior knowledge about joint angle configurations in the scope of 3-D human pose tracking. Training samples obtained from an industrial marker based tracking system are used for a nonparametric Parzen density estimation in the 12-dimensional joint configuration space. These learned probability densities constrain the image-driven joint angle estimates by drawing solutions towards familiar configurations. This prevents the method from producing unrealistic pose estimates due to unreliable image cues. Experiments on sequences with a human leg model reveal a considerably increased robustness, particularly in the presence of disturbed images and occlusions.
We gratefully acknowledge funding by the DFG project CR250/1 and the Max-Planck Center for visual computing and communication.
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References
Agarwal, A., Triggs, B.: Recovering 3D human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 44–58 (2006)
Bregler, C., Malik, J., Pullen, K.: Twist based acquisition and tracking of animal and human kinematics. International Journal of Computer Vision 56(3), 179–194 (2004)
Brox, T., Rosenhahn, B., Weickert, J.: Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Estimation. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 109–116. Springer, Heidelberg (2005)
Cremers, D., Osher, S., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. In: International Journal of Computer Vision (to appear)
Kadir, T., Brady, M.: Unsupervised non-parametric region segmentation using level sets. In: Proc. Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 1267–1274 (2003)
Kim, J., Fisher, J., Yezzi, A., Cetin, M., Willsky, A.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Transactions on Image Processing 14(10), 1486–1502 (2005)
Murray, R., Li, Z., Sastry, S.: Mathematical Introduction to Robotic Manipulation. CRC Press, Baton Rouge (1994)
Parzen, E.: On the estimation of a probability density function and the mode. Annals of Mathematical Statistics 33, 1065–1076 (1962)
Rosenblatt, F.: Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics 27, 832–837 (1956)
Rosenhahn, B., Brox, T., Kersting, U., Smith, A., Gurney, J., Klette, R.: A system for marker-less motion capture. Künstliche Intelligenz (1), 45–51 (2006)
Rosenhahn, B., Sommer, G.: Pose Estimation of Free-Form Objects. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 414–427. Springer, Heidelberg (2004)
Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: Proc. International Conference on Computer Vision and Pattern Recognition, Madison, WI, pp. 699–704 (June 2003)
Shakhnarovich, G., Viola, P., Darell, T.: Fast pose estimation with parameter sensitive hashing. In: Proc. International Conference on Computer Vision, Nice, France, pp. 750–757 (October 2003)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York (1986)
Sminchisescu, C., Jepson, A.: Generative modeling for continuous non-linearly embedded visual inference. In: Proc. International Conference on Machine Learning (2004)
Sminchisescu, C., Triggs, B.: Estimating articulated human motion with covariance scaled sampling. International Journal of Robotics Research 22(6), 371–391 (2003)
Zhang, J., Collins, R., Liu, Y.: Representation and matching of articulated shapes. In: Proc. International Conference on Computer Vision and Pattern Recognition, pp. 342–349 (June 2004)
Zhang, J., Collins, R., Liu, Y.: Bayesian body localization using mixture of nonlinear shape models. In: Proc. International Conference on Computer Vision, Beijing, China, pp. 725–732 (October 2005)
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Brox, T., Rosenhahn, B., Kersting, U.G., Cremers, D. (2006). Nonparametric Density Estimation for Human Pose Tracking. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_55
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DOI: https://doi.org/10.1007/11861898_55
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