Details
| Original language | English |
|---|---|
| Pages (from-to) | 1-18 |
| Number of pages | 18 |
| Journal | Journal of Mathematical Imaging and Vision |
| Volume | 28 |
| Issue number | 1 |
| Publication status | Published - 14 Jul 2007 |
| Externally published | Yes |
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.
Keywords
- Annealing, Interacting particle systems, Motion capture, Particle filtering
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Mathematics(all)
- Modelling and Simulation
- Physics and Astronomy(all)
- Condensed Matter Physics
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Mathematics(all)
- Geometry and Topology
- Mathematics(all)
- Applied Mathematics
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In: Journal of Mathematical Imaging and Vision, Vol. 28, No. 1, 14.07.2007, p. 1-18.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Interacting and annealing particle filters
T2 - Mathematics and a recipe for applications
AU - Gall, Jürgen
AU - Potthoff, Jürgen
AU - Schnörr, Christoph
AU - Rosenhahn, Bodo
AU - Seidel, Hans Peter
PY - 2007/7/14
Y1 - 2007/7/14
N2 - 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.
AB - 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.
KW - Annealing
KW - Interacting particle systems
KW - Motion capture
KW - Particle filtering
UR - http://www.scopus.com/inward/record.url?scp=34548282140&partnerID=8YFLogxK
U2 - 10.1007/s10851-007-0007-8
DO - 10.1007/s10851-007-0007-8
M3 - Article
AN - SCOPUS:34548282140
VL - 28
SP - 1
EP - 18
JO - Journal of Mathematical Imaging and Vision
JF - Journal of Mathematical Imaging and Vision
SN - 0924-9907
IS - 1
ER -