Sparse on-line Gaussian processes
Neural Computation
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
3D Tracking = Classification + Interpolation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Adaptive mixtures of local experts
Neural Computation
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
3D human pose from silhouettes by relevance vector regression
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Pose estimation with motionlet LLC coding
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
| Hi-index | 0.00 |
Within a discriminative framework for human pose estimation, modeling the mapping from feature space to pose space is challenging as we are required to handle the multimodal conditional distribution in a high-dimensional space. However, to build the mapping, current techniques usually involve a large set of training samples in the learning process but are limited in their capability to deal with multimodality. In this work, we propose a novel online sparse Gaussian Process (GP) regression model combining both temporal and spatial information. We exploit the fact that for a given test input, its output is mainly determined by the training samples potentially residing in its neighbor domain in the input-output unified space. This leads to a local mixture GP experts system, where the GP experts are defined in the local neighborhoods with the variational covariance function adapting to the specific regions. For the nonlinear human motion series, we integrate the temporal and spatial experts into a seamless system to handle multimodality. All the local experts are defined online within very small neighborhoods, so learning and inference are extremely efficient. We conduct extensive experiments on the real HumanEva database to verify the efficacy of the proposed model, obtaining significant improvement against the previous models.