Designing a camera placement assistance system for human motion capture based on a guided genetic algorithm

Designing a camera placement assistance system for human motion capture based on a guided genetic... In multi-camera motion capture systems, determining the optimal camera configuration (camera positions and orientations) is still an unresolved problem. At present, configurations are primarily guided by a human operator’s intuition, which requires expertise and experience, especially with complex, cluttered scenes. In this paper, we propose a solution to automate camera placement for motion capture applications in order to assist a human operator. Our solution is based on the use of a guided genetic algorithm to optimize camera network placement with an appropriate number of cameras. In order to improve the performance of the genetic algorithm (GA), two techniques are described. The first is a distribution and estimation technique, which reduces the search space and generates camera positions for the initial GA population. The second technique is an error metric, which is integrated at GA evaluation level as an optimization function to evaluate the quality of the camera placement in a camera network. Simulation experiments show that our approach is more efficient than other approaches in terms of computation time and quality of the final camera network. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Virtual Reality Springer Journals

Designing a camera placement assistance system for human motion capture based on a guided genetic algorithm

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Computer Science; Computer Graphics; Computing Methodologies; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; User Interfaces and Human Computer Interaction
ISSN
1359-4338
eISSN
1434-9957
D.O.I.
10.1007/s10055-017-0310-7
Publisher site
See Article on Publisher Site

Abstract

In multi-camera motion capture systems, determining the optimal camera configuration (camera positions and orientations) is still an unresolved problem. At present, configurations are primarily guided by a human operator’s intuition, which requires expertise and experience, especially with complex, cluttered scenes. In this paper, we propose a solution to automate camera placement for motion capture applications in order to assist a human operator. Our solution is based on the use of a guided genetic algorithm to optimize camera network placement with an appropriate number of cameras. In order to improve the performance of the genetic algorithm (GA), two techniques are described. The first is a distribution and estimation technique, which reduces the search space and generates camera positions for the initial GA population. The second technique is an error metric, which is integrated at GA evaluation level as an optimization function to evaluate the quality of the camera placement in a camera network. Simulation experiments show that our approach is more efficient than other approaches in terms of computation time and quality of the final camera network.

Journal

Virtual RealitySpringer Journals

Published: Apr 4, 2017

References

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