Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning to Estimate Human Pose with Data Driven Belief Propagation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering Human Body Configurations Using Pairwise Constraints between Parts
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
International Journal of Computer Vision
Inverse Kinematics Using Sequential Monte Carlo Methods
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple frame motion inference using belief propagation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Predicting 3d people from 2d pictures
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Human pose tracking using multi-level structured models
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
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Efficient monocular human pose tracking in dynamic scenes is an important problem. Existing pose tracking methods either use activity priors to restrict the search space, or use generative body models with weak kinematic constraints to infer pose over multiple frames; these often tends to be slow. We develop an efficient algorithm to track human pose by estimating multi-frame body dynamics without activity priors. We present a monte-carlo approximation of the body dynamics using spatio-temporal distributions over part tracks. To obtain tracks that favor kinematically feasible body poses, we propose a novel "kinematically constrained" particle filtering approach which results in more accurate pose tracking than other stochastic approaches that use single frame priors. We demonstrate the effectiveness of our approach on videos with actors performing various actions in indoor dynamic scenes.