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A Model-Based Approach for Estimating Human 3D Poses in Static Images
June 2006 (vol. 28 no. 6)
pp. 905-916
Estimating human body poses in static images is important for many image understanding applications including semantic content extraction and image database query and retrieval. This problem is challenging due to the presence of clutter in the image, ambiguities in image observation, unknown human image boundary, and high-dimensional state space due to the complex articulated structure of the human body. Human pose estimation can be made more robust by integrating the detection of body components such as face and limbs, with the highly constrained structure of the articulated body. In this paper, a data-driven approach based on Markov chain Monte Carlo (DD-MCMC) is used, where component detection results generate state proposals for 3D pose estimation. To translate these observations into pose hypotheses, we introduce the use of "proposal maps,” an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. Experimental results on a set of test images show that the method is able to estimate the human pose in static images of real scenes.

[1] A. Agarwal and B. Triggs, “3D Human Pose from Silhouettes by Relevance Vector Regression,” Proc. Computer Vision and Pattern Recognition Conf., pp. 882-888, 2004.
[2] C. Barron and I.A. Kakadiaris, “On the Improvement of Anthropometry and Pose Estimation from a Single Uncalibrated Image,” Machine Vision and Applications, pp. 229-236, 2003.
[3] C. Bregler and J. Malik, “Tracking People with Twists and Exponential Maps,” Proc. Computer Vision and Pattern Recognition Conf., pp. 8-15, 1998.
[4] C. Bregler, J. Malik, and K. Pullen, “Twist Based Acquisition and Tracking of Animal and Human Kinematics,” Int'l J. Computer Vision, vol. 56, no. 3, pp. 179-194, 2004.
[5] K. Choo and D.J. Fleet, “People Tracking with Hybrid Monte Carlo,” Proc. Int'l Conf. Computer Vision, pp. 321-328, 2001.
[6] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[7] Q. Delamarre and O. Faugeras, “3D Articulated Models and Multi-View Tracking with Physical Forces,” Computer Vision and Image Understanding, vol. 81, pp. 328-357, 2001.
[8] D. Demirdjian, “Combining Geometric- and View-Based Approaches for Articulated Pose Estimation,” Proc. European Conf. Computer Vision, pp. 183-194, 2004.
[9] J. Deutscher, A. Davison, and I. Reid, “Automatic Partitioning of High Dimensional Search Spaces Associated with Articulated Body Motion Capture,” Proc. Computer Vision and Pattern Recognition Conf., pp. 669-676, 2001.
[10] P.F. Felzenszwalb and D.P. Huttenlocher, “Efficient Matching of Pictorial Structures,” Proc. Computer Vision and Pattern Recognition Conf., pp. 2066-2073, 2000.
[11] D.M. Gavrila, “The Visual Analysis of Human Movement: A Survey,” Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82-97, Jan. 1999.
[12] W. Gilks, S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice. Chapman and Hall, 1996.
[13] K. Grauman, G. Shaknarovich, and T. Darrell, “Inferring 3D Structure with a Statistical Image-Based Shape Model,” Proc. Int'l Conf. Computer Vision, pp. 641-648, 2003.
[14] I. Haritaoglu, D. Harwood, and L. Davis, “W4: Real-Time Surveillance of People and Their Activities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug. 2000.
[15] S. Ioffe and D.A. Forsyth, “Probabilistic Methods for Finding People,” Int'l J. Computer Vision, vol. 43, no. 1, pp. 45-68, June 2001.
[16] M. Lee and I. Cohen, “Proposal Maps Driven MCMC for Estimating Human Body Pose in Static Images,” Proc. Computer Vision and Pattern Recognition Conf., pp. 334-341, 2004.
[17] M. Lee and I. Cohen, “Human Upper Body Pose Estimation in Static Images,” Proc. European Conf. Computer Vision, pp. 126-138, 2004.
[18] M. Lee and R. Nevatia, “Dynamic Human Pose Estimation Using Markov Chain Monte Carlo Approach,” MOTION, pp. 168-175, 2005.
[19] T.B. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231-268, 2001.
[20] A. Mohan, C. Parageogiou, and T. Poggio, “Example-Based Object Detection in Image by Components,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, Apr. 2001.
[21] G. Mori and J. Malik, “Estimating Human Body Configurations Using Shape Context Matching,” Proc. European Conf. Computer Vision, pp 666-680, 2002.
[22] G. Mori, X. Ren, A. Efros, and J. Malik, “Recovering Human Body Configurations: Combining Segmentation and Recognition,” Proc. Computer Vision and Pattern Recognition Conf., pp. 326-333, 2004.
[23] C. Papageorgiou, T. Evgeniou, and T. Poggio, “A Trainable Pedestrian Detection System,” Proc. IEEE Intelligent Vehicles Symp., pp. 241-246, 1998.
[24] D. Ramanan and D.A. Forsyth, “Finding and Tracking People from the Bottom Up,” Proc. Computer Vision and Pattern Recognition Conf., pp. 467-474, 2003.
[25] T.J. Roberts, S.J. McKenna, and I.W. Ricketts, “Human Pose Estimation Using Learnt Probabilistic Region Similarities and Partial Configurations,” Proc. European Conf. Computer Vision, pp. 291-303, 2004.
[26] R. Ronfard, C. Schmid, and B. Triggs, “Learning to Parse Pictures of People,” Proc. European Conf. Computer Vision, pp. 700-714, 2002.
[27] R. Rosales, M. Siddiqui, J. Alon, and S. Sclaroff, ”Estimating 3D Body Pose Using Uncalibrated Cameras,” Proc. Computer Vision and Pattern Recognition Conf., pp. 821-827, 2001.
[28] G. Shakhnarovich, P. Viola, and T. Darrell, “Fast Pose Estimation with Parameter Sensitive Hashing,” Proc. Int'l Conf. Computer Vision, pp. 750-759, 2003.
[29] C. Sminchisescu and B. Triggs, “Covariance Scaled Sampling for Monocular 3D Body Tracking,” Proc. Computer Vision and Pattern Recognition Conf., pp. 447-454, 2001.
[30] C. Sminchisescu and B. Triggs, “Kinematic Jump Processes for Monocular Human Tracking,” Proc. Computer Vision and Pattern Recognition Conf., pp. 69-76, 2003.
[31] C.J. Taylor, “Reconstruction of Articulated Objects from Point Correspondences in a Single Uncalibrated Image,” Computer Vision and Image Understanding, vol. 80, no. 3, pp. 349-363, 2000.
[32] Z.W. Tu and S.C. Zhu, “Image Segmentation by Data-Driven Markov Chain Monte Carlo,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 657-672, May 2002.
[33] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. Computer Vision and Pattern Recognition Conf., pp. 511-518, 2001.
[34] P. Viola, M.J. Jones, and D. Snow, “Detecting Pedestrians Using Patterns of Motion and Appearance,” Proc. Int'l Conf. Computer Vision, pp. 734-741, 2003.
[35] S. Zhu, R. Zhang, and Z. Tu, “Integrating Bottom-Up/Top-Down for Object Recognition by Data Driven Markov Chain Monte Carlo,” Proc. Computer Vision and Pattern Recognition Conf., pp. 1738-1745, 2000.
[36] T. Zhao and R. Nevatia, “Bayesian Human Segmentation in Crowded Situations,” Proc. Computer Vision and Pattern Recognition Conf., pp. 459-466, 2003.

Index Terms:
Three-dimensional human pose estimation from static images, body parts detector, data driven Markov chain Monte Carlo, generative models.
Citation:
Mun Wai Lee, Isaac Cohen, "A Model-Based Approach for Estimating Human 3D Poses in Static Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 905-916, June 2006, doi:10.1109/TPAMI.2006.110
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