Single camera 3D human pose estimation: A Review of current techniques

Vision-based techniques to estimate 3D human pose from single camera are important in many applications like entertainment and games industries, sports science, gait analysis and video surveillance. While de-facto optical-based motion capture system is highly accurate in estimating 3D human pose, vision-based techniques offer better alternative because the latter are far cheaper and non-intrusive. In this paper, we review recent significant advances made in single-camera 3D human pose estimation techniques. We discuss about challenges faced, typical image observations used, ways to address the challenges and highlighting limitations in state-of-the-art. We conclude this paper with speculation of future research directions as well as open research problems.

[1]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[2]  David J. Fleet,et al.  Gaussian Process Dynamical Models , 2005, NIPS.

[3]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[4]  Michael Isard,et al.  Tracking loose-limbed people , 2004, CVPR 2004.

[5]  Cristian Sminchisescu,et al.  Covariance scaled sampling for monocular 3D body tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  D. Forsyth,et al.  Tracking People by Learning Their Appearance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..

[8]  David J. Fleet,et al.  3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Nicholas R. Howe,et al.  Silhouette Lookup for Automatic Pose Tracking , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[10]  Michael Isard,et al.  Nonparametric belief propagation , 2010, Commun. ACM.

[11]  Jitendra Malik,et al.  Recovering 3D human body configurations using shape contexts , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[13]  David J. Fleet,et al.  Temporal motion models for monocular and multiview 3D human body tracking , 2006, Comput. Vis. Image Underst..

[14]  Ian D. Reid,et al.  Articulated Body Motion Capture by Stochastic Search , 2005, International Journal of Computer Vision.

[15]  David A. Forsyth,et al.  Strike a pose: tracking people by finding stylized poses , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[17]  Cristian Sminchisescu,et al.  Generative modeling for continuous non-linearly embedded visual inference , 2004, ICML.

[18]  James F. O'Brien,et al.  Computational Studies of Human Motion , 2006 .

[19]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[20]  Michael J. Black,et al.  Implicit Probabilistic Models of Human Motion for Synthesis and Tracking , 2002, ECCV.

[21]  Ankur Agarwal,et al.  Tracking Articulated Motion Using a Mixture of Autoregressive Models , 2004, ECCV.

[22]  M. Lee,et al.  Proposal maps driven MCMC for estimating human body pose in static images , 2004, CVPR 2004.

[23]  Ramakant Nevatia,et al.  Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[24]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Cristian Sminchisescu 3D Human Motion Analysis in Monocular Video Techniques and Challenges , 2006, AVSS.

[26]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[27]  Luc Van Gool,et al.  Markerless tracking of complex human motions from multiple views , 2006, Comput. Vis. Image Underst..

[28]  Cristian Sminchisescu,et al.  Kinematic jump processes for monocular 3D human tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[29]  Zhuowen Tu,et al.  Image Segmentation by Data-Driven Markov Chain Monte Carlo , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  David A. Forsyth,et al.  Finding and tracking people from the bottom up , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[31]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[32]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion , 2006 .

[33]  A. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, CVPR 2004.

[34]  Aaron Hertzmann,et al.  Style-based inverse kinematics , 2004, ACM Trans. Graph..

[35]  David A. Forsyth,et al.  Computational Studies of Human Motion: Part 1, Tracking and Motion Synthesis , 2005, Found. Trends Comput. Graph. Vis..

[36]  William T. Freeman,et al.  Bayesian Reconstruction of 3D Human Motion from Single-Camera Video , 1999, NIPS.

[37]  Hans-Peter Seidel,et al.  Free-viewpoint video of human actors , 2003, ACM Trans. Graph..

[38]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[39]  Ramakant Nevatia,et al.  Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[41]  Cristian Sminchisescu,et al.  BM³E : Discriminative Density Propagation for Visual Tracking , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  David A. Forsyth,et al.  Searching for Complex Human Activities with No Visual Examples , 2008, International Journal of Computer Vision.

[43]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[44]  Luc Van Gool,et al.  Learning Generative Models for Multi-Activity Body Pose Estimation , 2008, International Journal of Computer Vision.

[45]  Sidharth Bhatia,et al.  Tracking loose-limbed people , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[46]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[47]  David J. Fleet,et al.  Priors for people tracking from small training sets , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[48]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[49]  David A. Forsyth,et al.  Automatic Annotation of Everyday Movements , 2003, NIPS.

[50]  Ahmed M. Elgammal,et al.  Modeling View and Posture Manifolds for Tracking , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[51]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[52]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).