RESEARCH ARTICLE


Early Fire Smoke Image Segmentation in a Complex Large Space



Hu Yan*, 1, 2, Wang Huiqin1, 2, Zhao Qian3, Lu Ying2
1 School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, 710055, China
2 School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, 710055, China
3 Xi'an Modern Control Technology Research Institute, Xi’an, Shaanxi, 710065, China


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Creative Commons License
© 2015 Yan et al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi, 710055, China; Tel: 13772162942; E-mail: huyan.nancy@163.com


Abstract

Aiming at the problem that the fire smoke image segmentation algorithm cannot obtain integral smoke information, the adaptive background updating smoke foreground object extraction algorithm based on block segmentation is proposed. According to the characteristics that the smoke internal pixels continuously roll in the heat of the driver and the pixel concentration from near the fire source to edge decreases in turn, the method for different blocks using different difference threshold is adopted to ensure the integrity of the extracted foreground target. Under the difference between the current frame and the background frame, block difference thresholds are updated to let the differential threshold adjustment with continuous adaptive monitoring. In the new weighted average updated background image corrected by the original background image, the interference suspicious target edge pixels are removed. Simulation experimental results show that the method is able to extract more complete information of smoke in a suspected area of the edge, and eliminate the interference of light and pedestrian in complex space.

Keywords: Background updating, blocking, complex large space, connected domain, smoke image segmentation.