Fire detection for early fire alarm based on optical flow video processing

2012 ◽  
Author(s):  
Suchet Rinsurongkawong ◽  
Mongkol Ekpanyapong ◽  
Matthew N. Dailey
Keyword(s):  
Optical Flow ◽  
Video Processing ◽  
Fire Detection ◽  
Fire Alarm

scholarly journals Computer Vision-based Early Fire Detection Using Enhanced Chromatic Segmentation and Optical Flow Analysis Technique

2020 ◽  
Vol 17 (6) ◽  
pp. 947-953
Author(s):  
Arnisha Khondaker ◽  
Arman Khandaker ◽  
Jia Uddin
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Forest Fires ◽  
Video Processing ◽  
Flow Analysis ◽  
Fire Detection ◽  
Majority Voting ◽  
False Alarms ◽  
Processing Technologies ◽  
Proposed Model

Recent advances in video processing technologies have led to a wave of research on computer vision-based fire detection systems. This paper presents a multi-level framework for fire detection that analyses patterns in chromatic information, shape transmutation, and optical flow estimation of fire. First, the decision function of fire pixels based on chromatic information uses majority voting among state-of-the-art fire color detection rules to extract the regions of interest. The extracted pixels are then verified for authenticity by examining the dynamics of shape. Finally, a measure of turbulence is assessed by an enhanced optical flow analysis algorithm to confirm the presence of fire. To evaluate the performance of the proposed model, we utilize videos from the Mivia and Zenodo datasets, which have a diverse set of scenarios including indoor, outdoor, and forest fires, along with videos containing no fire. The proposed model exhibits an average accuracy of 97.2% for our tested dataset. In addition, the experimental results demonstrate that the proposed model significantly reduces the rate of false alarms compared to the other existing models

Sign in / Sign up

Export Citation Format

Share Document