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Articulated body motion capture by annealed particle filtering

Deutscher, J., Blake, A., Reid, I.·2002·Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)

Abstract

The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints: either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, we aim for general tracking without special preparation of objects or restrictive assumptions. The principal contribution of the paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle based on annealing to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently.

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Metadata

Authors
Deutscher, J., Blake, A., & Reid, I.
Venue
Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
Published
2002-11-07
Volume
2
Pages
126-133
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2144124341
Cited by
868
References
14
FWCI
40.42
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Articulated body motion capture by annealed particle filtering. https://app.cochat.ai/papers/10.1109/cvpr.2000.854758
The paper
Deutscher, J., Blake, A., & Reid, I. (2002). Articulated body motion capture by annealed particle filtering. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). https://doi.org/10.1109/cvpr.2000.854758

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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