Convolutional Neural Networks Based Fire Detection in Surveillance Videos

The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this re...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.18174-18183
Main Authors: Muhammad, Khan, Ahmad, Jamil, Mehmood, Irfan, Rho, Seungmin, Baik, Sung Wook
Format: Article
Language:English
Subjects:
Artificial neural networks
CCTV video analysis
Color
Computer architecture
Computing time
Convolution
deep learning
Feature extraction
Fire detection
Fires
image classification
Model accuracy
Neural networks
real-world applications
Scientific papers
Surveillance
Surveillance systems
Video
Videos
Vision systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The recent advances in embedded processing have enabled the vision based systems to detect fire during surveillance using convolutional neural networks (CNNs). However, such methods generally need more computational time and memory, restricting its implementation in surveillance networks. In this research paper, we propose a cost-effective fire detection CNN architecture for surveillance videos. The model is inspired from GoogleNet architecture, considering its reasonable computational complexity and suitability for the intended problem compared to other computationally expensive networks such as AlexNet. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in CCTV surveillance systems compared to state-of-the-art methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2812835