Video-Based People Tracking

Vision-based human pose tracking promises to be a key enabling technology for myriad applications, including the analysis of human activities for perceptive environments and novel man-machine interfaces. While progress toward that goal has been exciting, and limited applications have been demonstrated, the recovery of human pose from video in unconstrained settings remains challenging. One of the key challenges stems from the complexity of the human kinematic structure itself. The sheer number and variety of joints in the human body (the nature of which is an active area of biomechanics research) entails the estimation of many parameters. The estimation problem is also challenging because muscles and other body tissues obscure the skeletal structure, making it impossible to directly observe the pose of the skeleton.

[1]  David J. Fleet,et al.  People tracking using hybrid Monte Carlo filtering , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[2]  Yihong Gong,et al.  Latent Pose Estimator for Continuous Action Recognition , 2008, ECCV.

[3]  Cristian Sminchisescu,et al.  Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Radford M. Neal Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[5]  Cristian Sminchisescu,et al.  Fast algorithms for large scale conditional 3D prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Vladimir Pavlovic,et al.  A dynamic Bayesian network approach to figure tracking using learned dynamic models , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Hans-Peter Seidel,et al.  Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications , 2007, Journal of Mathematical Imaging and Vision.

[8]  Hans-Hellmut Nagel,et al.  3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value Gradients , 1997, International Journal of Computer Vision.

[9]  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).

[10]  Richard Catrambone,et al.  Presenting Movement in a Computer-Based Dance Tutor , 2003, Int. J. Hum. Comput. Interact..

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

[12]  Michael J. Black,et al.  Combined discriminative and generative articulated pose and non-rigid shape estimation , 2007, NIPS.

[13]  Jack B. Kuipers,et al.  Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality , 2002 .

[14]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[15]  Rui Li,et al.  Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[16]  Pascal Fua,et al.  Articulated Soft Objects for Video-based Body Modeling , 2001, ICCV.

[17]  Larry S. Davis,et al.  A Robust Background Subtraction and Shadow Detection , 1999 .

[18]  Sebastian Thrun,et al.  SCAPE: shape completion and animation of people , 2005, SIGGRAPH 2005.

[19]  Camillo J. Taylor,et al.  Reconstruction of Articulated Objects from Point Correspondences in a Single Uncalibrated Image , 2000, Comput. Vis. Image Underst..

[20]  Hans-Hellmut Nagel,et al.  Tracking Persons in Monocular Image Sequences , 1999, Comput. Vis. Image Underst..

[21]  AgarwalAnkur,et al.  Recovering 3D Human Pose from Monocular Images , 2006 .

[22]  Robert C. Bolles,et al.  Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching , 1977, IJCAI.

[23]  Andrew W. Fitzgibbon,et al.  The Joint Manifold Model for Semi-supervised Multi-valued Regression , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  Andrew Blake,et al.  Using expectation-maximisation to learn dynamical models from visual data , 1999, Image Vis. Comput..

[25]  David J. Fleet,et al.  The Kneed Walker for human pose tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Paul A. Viola,et al.  Learning silhouette features for control of human motion , 2004, SIGGRAPH '04.

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

[28]  David J. Fleet,et al.  Probabilistic tracking of motion boundaries with spatiotemporal predictions , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[30]  Cristian Sminchisescu,et al.  Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[32]  Takeo Kanade,et al.  Model-based tracking of self-occluding articulated objects , 1995, Proceedings of IEEE International Conference on Computer Vision.

[33]  Radford M. Neal Annealed importance sampling , 1998, Stat. Comput..

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

[35]  Trevor Darrell,et al.  Sparse probabilistic regression for activity-independent human pose inference , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  C. Cobelli,et al.  A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach , 2006, Annals of Biomedical Engineering.

[37]  Alex Pentland,et al.  Dynamic models of human motion , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

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

[39]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

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

[41]  David Harwood A statistical approach for real time robust background subtraction , 1999 .

[42]  Dimitris N. Metaxas,et al.  Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  David J. Fleet Motion Models for People Tracking , 2011, Visual Analysis of Humans.

[44]  Michael J. Black,et al.  Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[45]  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).

[46]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[47]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[48]  Cristian Sminchisescu,et al.  Spectral Latent Variable Models for Perceptual Inference , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[50]  Jun S. Liu,et al.  Sequential Imputations and Bayesian Missing Data Problems , 1994 .

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

[52]  Michael J. Black,et al.  Detailed Human Shape and Pose from Images , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[53]  Mohan M. Trivedi,et al.  Detecting Moving Shadows: Algorithms and Evaluation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[54]  David J. Fleet,et al.  Physics-Based Person Tracking Using Simplified Lower-Body Dynamics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[55]  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.

[56]  Björn Stenger,et al.  Model-based hand tracking using a hierarchical Bayesian filter , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

[60]  David J. Fleet,et al.  Model-based hand tracking with texture, shading and self-occlusions , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[61]  Ioannis A. Kakadiaris,et al.  Model-Based Estimation of 3D Human Motion , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[63]  Hans-Peter Seidel,et al.  Cloth X-Ray: MoCap of People Wearing Textiles , 2006, DAGM-Symposium.

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

[65]  Camillo J. Taylor,et al.  Reconstruction of articulated objects from point correspondences in a single uncalibrated image , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[66]  Trevor Darrell,et al.  Untethered gesture acquisition and recognition for virtual world manipulation , 2005, Virtual Reality.

[67]  Cristian Sminchisescu,et al.  Discriminative density propagation for 3D human motion estimation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[68]  Larry S. Davis,et al.  3-D model-based tracking of humans in action: a multi-view approach , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[69]  Nicholas R. Howe,et al.  Silhouette lookup for monocular 3D pose tracking , 2007, Image Vis. Comput..

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

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

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

[73]  David J. Fleet,et al.  Robust Online Appearance Models for Visual Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  Michael J. Black,et al.  The Naked Truth: Estimating Body Shape Under Clothing , 2008, ECCV.

[75]  Pascal Fua,et al.  Hierarchical implicit surface joint limits for human body tracking , 2005, Comput. Vis. Image Underst..

[76]  F. Sebastian Grassia,et al.  Practical Parameterization of Rotations Using the Exponential Map , 1998, J. Graphics, GPU, & Game Tools.

[77]  CipollaRoberto,et al.  Model-Based Hand Tracking Using a Hierarchical Bayesian Filter , 2006 .

[78]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

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

[80]  Rómer Rosales,et al.  Learning Body Pose via Specialized Maps , 2001, NIPS.

[81]  Odest Chadwicke Jenkins,et al.  Physical simulation for probabilistic motion tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[82]  David J. Fleet,et al.  Hybrid Monte Carlo filtering: edge-based people tracking , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..