Human tracking using floor sensors based on the Markov chain Monte Carlo method
Human tracking using floor sensors based on the Markov chain Monte Carlo method
复制标题
基于马尔可夫链蒙特卡罗方法的使用地板传感器的人体跟踪
DOI：
10.1109/icpr.2004.1333922
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
H. Ishiguro
中科院分区：
文献类型：
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作者：
Takuya Murakita;Tetsushi Ikeda;H. Ishiguro
本文的目的是开发一个人体跟踪系统，是抗环境变化，覆盖范围广。结构简单的地板传感器成本低，可以在广泛的区域内跟踪人。然而，传感器的阅读是离散和缺失的;因此，脚步并不代表人的精确位置。马尔可夫链蒙特卡罗方法（MCMC）是一种很有前途的跟踪算法，这类信号。我们应用两个预测模型的MCMC：线性高斯模型和高度非线性双足模型。高斯模型在计算成本方面很有效，而双足模型比高斯模型更准确地区分人。高斯模型可用于跟踪多个人，而双足模型可用于需要更精确跟踪的情况。
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.