Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Hybrid Monte Carlo Filtering: Edge-Based People Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Particle Filter with Analytical Inference for Human Body Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
The Journal of Machine Learning Research
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
Fast particle smoothing: if I had a million particles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Conditional Random People: Tracking Humans with CRFs and Grid Filters
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Measurement Function Design for Visual Tracking Applications
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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
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
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
Monte Carlo filtering and smoothing with application to time-varying spectral estimation
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A decentralized probabilistic approach to articulated body tracking
Computer Vision and Image Understanding
Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational mixture smoothing for non-linear dynamical systems
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Guest Editorial: State of the Art in Image- and Video-Based Human Pose and Motion Estimation
International Journal of Computer Vision
Real time multiple people tracking and pose estimation
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Integrating multiple uncalibrated views for human 3D pose estimation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
Multiple people tracking and pose estimation with occlusion estimation
Computer Vision and Image Understanding
3D Human model adaptation by frame selection and shape-texture optimization
Computer Vision and Image Understanding
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
Two-layer dual gait generative models for human motion estimation from a single camera
Image and Vision Computing
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Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate inference is employed to tractably estimate the high-dimensional (~30D) posture space. Of these approximate inference techniques, particle filtering is the most commonly used approach. However filtering only takes into account past observations--almost no body tracking research employs smoothing to improve the filtered inference estimate, despite the fact that smoothing considers both past and future evidence and so should be more accurate. In an effort to objectively determine the worth of existing smoothing algorithms when applied to human body tracking, this paper investigates three approximate smoothed-inference techniques: particle-filtered backwards smoothing, variational approximation and Gibbs sampling. Results are quantitatively evaluated on both the HumanEva dataset as well as a scene containing occluding clutter. Surprisingly, it is found that existing smoothing techniques are unable to provide much improvement on the filtered estimate, and possible reasons as to why are explored and discussed.