Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Building Roadmaps of Local Minima of Visual Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Avoiding the "Streetlight Effect": Tracking by Exploring Likelihood Modes
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Local distance preservation in the GP-LVM through back constraints
ICML '06 Proceedings of the 23rd international conference on Machine learning
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Temporal motion models for monocular and multiview 3D human body tracking
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Variable-mass particle filter for road-constrained vehicle tracking
EURASIP Journal on Advances in Signal Processing
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
Guest Editorial: State of the Art in Image- and Video-Based Human Pose and Motion Estimation
International Journal of Computer Vision
International Journal of Computer Vision
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Behavioural analysis with movement cluster model for concurrent actions
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
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Tracking unknown human motions using generative tracking techniques requires the exploration of a high-dimensional pose space which is both difficult and computationally expensive. Alternatively, if the type of activity is known and training data is available, a low-dimensional latent pose space may be learned and the difficulty and cost of the estimation task reduced. In this paper we attempt to combine the competing benefits-flexibility and efficiency-of these two generative tracking scenarios within a single approach. We define a number of ''activity models'', each composed of a pose space with unique dimensionality and an associated dynamical model, and each designed for use in the recovery of a particular class of activity. We then propose a method for the fair combination of these activity models for use in particle dispersion by an annealed particle filter. The resulting algorithm, which we term multiple activity model annealed particle filtering (MAM-APF), is able to dynamically vary the scope of its search effort, using a small number of particles to explore latent pose spaces and a large number of particles to explore the full pose space. We present quantitative results on the HumanEva-I and HumanEva-II datasets, demonstrating robust 3D tracking of known and unknown activities from fewer than four cameras.