Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications

Authors:
Jürgen Gall;Jürgen Potthoff;Christoph Schnörr;Bodo Rosenhahn;Hans-Peter Seidel
Affiliations:
Max-Planck-Institute for Computer Science, Saarbrücken, Germany;Dept. M & CS, Stochastics Group, University of Mannheim, Mannheim, Germany;Dept. M & CS, CVGPR Group, University of Heidelberg, Heidelberg, Germany;Max-Planck-Institute for Computer Science, Saarbrücken, Germany;Max-Planck-Institute for Computer Science, Saarbrücken, Germany
Venue:
Journal of Mathematical Imaging and Vision
Year:
2007

Citing 7
Cited 8

Annealed importance sampling

Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Articulated Body Motion Capture by Stochastic Search

International Journal of Computer Vision
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
Stochastic simulation algorithms for dynamic probabilistic networks

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning for multi-view 3d tracking in the context of particle filters

ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
A survey of convergence results on particle filtering methods forpractitioners

IEEE Transactions on Signal Processing

Optimization and Filtering for Human Motion Capture

International Journal of Computer Vision
Clustered stochastic optimization for object recognition and pose estimation

Proceedings of the 29th DAGM conference on Pattern recognition
Particle swarm optimization with soft search space partitioning for video-based markerless pose tracking

ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Latent hough transform for object detection

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Parametric annealing: A stochastic search method for human pose tracking

Pattern Recognition
Tracking in action space

ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Tracking in object action space

Computer Vision and Image Understanding
A new hierarchical method for markerless human pose estimation

ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Interacting and annealing are two powerful strategies that are applied in different areas of stochastic modelling and data analysis. Interacting particle systems approximate a distribution of interest by a finite number of particles where the particles interact between the time steps. In computer vision, they are commonly known as particle filters. Simulated annealing, on the other hand, is a global optimization method derived from statistical mechanics. A recent heuristic approach to fuse these two techniques for motion capturing has become known as annealed particle filter. In order to analyze these techniques, we rigorously derive in this paper two algorithms with annealing properties based on the mathematical theory of interacting particle systems. Convergence results and sufficient parameter restrictions enable us to point out limitations of the annealed particle filter. Moreover, we evaluate the impact of the parameters on the performance in various experiments, including the tracking of articulated bodies from noisy measurements. Our results provide a general guidance on suitable parameter choices for different applications.