Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On large scale nonlinear network optimization
Mathematical Programming: Series A and B
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Improving the mean field approximation via the use of mixture distributions
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
A Smoothing Filter for CONDENSATION
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Bundle Adjustment - A Modern Synthesis
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Variational Learning for Switching State-Space Models
Neural Computation
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Building Roadmaps of Minima and Transitions in Visual Models
International Journal of Computer Vision
Dynamic Human Pose Estimation using Markov Chain Monte Carlo Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Conditional models for contextual human motion recognition
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
BM3E: Discriminative Density Propagation for Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast mixing hyperdynamic sampling
Image and Vision Computing
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
International Journal of Computer Vision
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We present an algorithm for computing joint state, smoothed, density estimates for non-linear dynamical systems in a Bayesian setting. Many visual tracking problems can be formulated as probabilistic inference over time series, but we are not aware of mixture smoothers that would apply to weakly identifiable models, where multimodality is persistent rather than transient (e.g. monocular 3D human tracking). Such processes, in principle, exclude iterated Kalman smoothers, whereas flexible MCMC methods or sample based particle smoothers encounter computational difficulties: accurately locating an exponential number of probable joint state modes representing high-dimensional trajectories, rapidly mixing between those or resampling probable configurations missed during filtering. In this paper we present an alternative, layered, mixture density smoothing algorithm that exploits the accuracy of efficient optimization within a Bayesian approximation framework. The distribution is progressively refined by combining polynomial time search over the embedded network of temporal observation likelihood peaks, MAP continuous trajectory estimates, and Bayesian variational adjustment of the resulting joint mixture approximation. Our results demonstrate the effectiveness of the method on the problem of inferring multiple plausible 3D human motion trajectories from monocular video.