Earticle

초록

영어

To meet the demand for the early location of fire in large-span space buildings, an accurate fire location method is proposed based on machine vision technology. A nonlinear implicit camera calibration method is proposed by combining an improved particle swarm optimization (PSO) method with least squares support vector machine (LS-SVM) to solve the problem that it is difficult to establish accurate mathematical models for traditional nonlinear explicit camera calibration. The matched pixel coordinates of images collected by cameras are used as input, and the output is the world coordinates. The IPSO is used to search the optimal parameters of LS-SVM regression model to increase the convergence speed and improve the generalization ability of LS-SVM. The spatial location of fire is achieved by three-dimensional reconstruction. The proposed method is applied to fire location for high and large buildings, and experimental results show that the method is effective, fast and accurate.

목차

Abstract
 1. Introduction
 2. Basic Theories
  2.1 Camera Imaging Model
  2.2 Improving PSO
  2.3 LS-SVM Theory
  2.4 IPSO for LS-SVM Parameter Optimization
  2.5 LS-SVM Prediction Model based on IPSO
 3. Experimental Analysis
  3.1 Calibration Experiment
  3.2 Calculation of 3D Position of Fire Source
 5. Conclusions
 References

키워드

저자

• Lu Ying [ School of Architecture, Xi'an Univ. of Arch＆Tech., Xi'an , Shanxi, P.R. China / School of Information and Control Engineering, Xi'an Univ. of Arch＆Tech., Xi'an , Shanxi, P.R. China ]
• Wang Huiqin [ School of Information and Control Engineering, Xi'an Univ. of Arch＆Tech., Xi'an , Shanxi, P.R. China ]
• Wang Ke [ School of Information and Control Engineering, Xi'an Univ. of Arch＆Tech., Xi'an , Shanxi, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605