Distributed Particle Filtering for Multiocular Soccer-Ball Tracking

This paper proposes a distributed state estimation architecture for multi-sensor fusion. The system consists of networked subsystems that cooperatively estimate the state of a common target from their own observations. Each subsystem is equipped with a self-contained particle filter that can operate...

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Bibliographic Details
Main Authors: Misu, T., Matsui, A., Naemura, M., Fujii, M., Yagi, N.
Format: Conference Proceeding
Language:English
Subjects:
Broadcasting
Cameras
Distributed tracking
dynamics
Information filtering
Information filters
Intelligent robots
Particle filters
Particle tracking
position measurement
Robustness
State estimation
Target tracking
tracking filters
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Summary:This paper proposes a distributed state estimation architecture for multi-sensor fusion. The system consists of networked subsystems that cooperatively estimate the state of a common target from their own observations. Each subsystem is equipped with a self-contained particle filter that can operate in stand-alone as well as in network mode with a particle exchange function. We applied this flexible architecture to 3D soccer-ball tracking by modeling the imaging processes related to the centroid, size, and motion-blur of a target, and by modeling the dynamics with ballistic motion, bounce, and rolling. To evaluate the precision and robustness of the system, we conducted experiments using multiocular images of a professional soccer match.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.366835