Analysis of human behavior to a communication robot in an open field
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A probabilistic reasoning framework for smart homes
Proceedings of the 5th international workshop on Middleware for pervasive and ad-hoc computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Gaussian Process Person Identifier Based on Simple Floor Sensors
EuroSSC '08 Proceedings of the 3rd European Conference on Smart Sensing and Context
An affective guide robot in a shopping mall
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Dynamic tracking system for object recognition
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Tasking networked CCTV cameras and mobile phones to identify and localize multiple people
Proceedings of the 12th ACM international conference on Ubiquitous computing
A communication robot in a shopping mall
IEEE Transactions on Robotics
On optimal arrangements of binary sensors
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
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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.