.png){kind=icon}
.png){kind=icon}
Return
Multisensor data fusion for fire detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.inffus.2009.12.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fire is a common disastrous phenomenon that constitutes a serious threat. The SCIER (Sensor and Computing Infrastructure for Environmental Risks is partially funded by the European Community through the FP6 IST Program. The work presented in this paper expresses the ideas of the authors and not necessarily the whole SCIER consortium.) project envisages the deployment of Wireless Sensor Networks at the Urban-Rural-Interface (URI) aiming to the detection, monitoring and crisis management of such natural hazards. One of its primary objectives is the development of an advanced multisensor data fusion scheme which feeds a CUSUM sequential test used in the early detection of fires. Reasoning about the probability of fire in a geographical area covered by temperature, humidity and vision sensors is achieved through Evidential Reasoning (Dempster-Shafer theory). (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Fire detection
Multisensor data fusion
Dempster-Shafer theory
Cusum
Evidence combination rules
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.