Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICPR.2004.1333922 | Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference |
Keywords | Field | DocType |
Gaussian processes,Markov processes,Monte Carlo methods,pressure sensors,signal processing,tracking,Markov chain Monte Carlo method,floor sensors,human tracking system,linear Gaussian model,nonlinear bipedal model | Computer vision,Signal processing,Monte Carlo method,Markov process,Markov chain Monte Carlo,Markov model,Computer science,Tracking system,Gaussian network model,Gaussian process,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
4 | 1051-4651 | 0-7695-2128-2 |
Citations | PageRank | References |
31 | 2.87 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuya Murakita | 1 | 31 | 2.87 |
| Tetsushi Ikeda | 2 | 95 | 9.94 |
| Hiroshi Ishiguro | 3 | 4680 | 513.13 |