Abstract
Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts.
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Crandell, D., Felzenszwalb, P.F., Huttenlocher, D.P.: Spatial priors for part-based recognition using statistical models. In: IEEE CVPR (2005)
Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1), 55–79 (2003)
Forsyth, D.A., Arikan, O., Ikemoto, L., O’Brien, J., Ramanan, D.: Computational studies of human motion: Part 1, tracking and motion synthesis. Foundations and Trends in Computer Graphics and Vision 1(2/3), 77–254 (2006)
Gross, R., Shi, J.: The cmu motion of body(mobo) database. Technical Report CMU-RI-TR-01-18, CMU (2001)
Hogg, D.: Model-based vision: a program to see a walking person. Image and Vision Computing 1(1), 5–20 (1983)
Ioffe, S., Forsyth, D.: Human tracking with mixtures of trees. In: IEEE ICCV (2001)
Ju, S.X., Black, M.J., Yaccob, Y.: Cardboard people: A parameterized model of articulated image motion. In: Proc. Automatic Face and Gesture Recognition (1996)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML (2001)
Lan, X., Huttenlocher, D.P.: Beyond trees: Common-factor models for 2d human pose recovery. In: IEEE ICCV (2005)
Meila, M., Jordan, M.I.: Learning with mixtures of trees. Journal of Machine Learning Research 1, 1–48 (2000)
Mori, G., Malik, J.: Estimating human body configurations using shape context matching. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 666–680. Springer, Heidelberg (2002)
Ramanan, D.: Learning to parse images of articulated bodies. In: NIPS 19 (2007)
Ren, X., Berg, A., Malik, J.: Recovering human body configurations using pairwise constraints between parts. In: IEEE ICCV (2005)
Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter sensitive hashing. In: IEEE ICCV (2003)
Sigal, L., Black, M.J.: Measure locally, reason globally: Occlusion-sensitive articulated pose estimation. In: IEEE CVPR (2006)
Sminchisescu, C., Kanaujia, A., Metaxas, D.: BM3E: Discriminative Density Propagation for Visual Tracking. IEEE PAMI 29(11), 2030–2044 (2007)
Song, Y., Goncalves, L., Perona, P.: Unsupervised learning of human motion. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(7), 814–827 (2003)
Sudderth, E.B., Mandel, M.I., Freeman, W.T., Willsky, A.S.: Distributed occlusion reasoning for tracking with nonparametric belief propagation. In: NIPS (2004)
Sullivan, J., Carlsson, S.: Recognizing and tracking human action. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 629–644. Springer, Heidelberg (2002)
Torralba, A., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. In: NIPS 17 (2005)
Toyama, K., Blake, A.: Probabilistic exemplar-based tracking in a metric space. In: IEEE ICCV (2001)
Truyen, T.T., Phung, D.Q., Bui, H.H., Venkatesh, S.: AdaBoost.MRF: Boosted markov random forests and application to multilevel activity recognition. In: IEEE CVPR (2006)
Wainwright, M.J., Jaakkola, T.S., Willsky, A.S.: A new class of upper bounds on the log partition function. IEEE Transactions on Information Theory 51(7), 2313–2335 (2005)
Wang, Y., Mori, G.: Boosted multiple deformable trees for parsing human poses. In: ICCV Workshop on Human Motion Understanding, Modeling, Capture and Animation (2007)
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Wang, Y., Mori, G. (2008). Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_53
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DOI: https://doi.org/10.1007/978-3-540-88690-7_53
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