Human tracking using floor sensors based on the Markov chain Monte Carlo method

The aim of this paper is to develop a human tracking system that is resistant to environmental changes and covers wide area. Simply structured floor sensors are low-cost and can track people in a wide area. However, the sensor reading is discrete and missing; therefore, footsteps do not represent th...

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Bibliographic Details
Main Authors: Murakita, T., Ikeda, T., Ishiguro, H.
Format: Conference Proceeding
Language:English
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Humans
Immune system
Infrared sensors
Insulation life
Intelligent sensors
Legged locomotion
Predictive models
Sensor systems
Sensor systems and applications
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Summary:The aim of this paper is to develop a human tracking system that is resistant to environmental changes and covers wide area. Simply structured floor sensors are low-cost and can track people in a wide area. However, the sensor reading is discrete and missing; therefore, footsteps do not represent the precise location of a person. A Markov chain Monte Carlo method (MCMC) is a promising tracking algorithm for these kinds of signals. We applied two prediction models to the MCMC: a linear Gaussian model and a highly nonlinear bipedal model. The Gaussian model was efficient in terms of computational cost while the bipedal model discriminated people more accurate than the Gaussian model. The Gaussian model can be used to track a number of people, and the bipedal model can be used in situations where more accurate tracking is required.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2004.1333922