Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning to track 3D human motion from silhouettes
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning generative models for monocular body pose estimation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Nonparametric density estimation with adaptive, anisotropic kernels for human motion tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
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We present a novel approach to tracking 2D human motion in uncalibrated monocular videos. Human motion usually exhibits timevarying patterns, and we propose to use locally learnt prior models to capture this characteristics. For each input image, our method automatically learns a local probability density model and a local dynamical model from a set of training examples that are close matches to the input. We evaluate the image likelihood by matching a deformable 2D human body model to the input images. The local models and the image likelihood are integrated to optimize the pose for the current input. Experiments on both synthetic and real videos demonstrate the effectiveness of our method.