.png){kind=icon}
Return
Efficient Video Fire Detection Exploiting Motion-Flicker-Based Dynamic Features and Deep Static Features
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2991338.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Since fire is one of the most serious types of accidents that can occur, there is always a need for improvement in fire detection capabilities. Convolutional neural networks (CNNs) have been used for a variety of high-performance computer vision tasks. The use of CNNs to extract deep static features of fire has greatly improved the accuracy of fire detection. However, the implementation of CNNs in the real world is limited by their high computational cost. In addition, fire detection methods based on the classification of images alone using CNNs cannot account for the dynamic features of fire. Therefore, in this paper, a method that exploits both motion-flicker-based dynamic features and deep static features is proposed for video fire detection. First, dynamic features are extracted by analyzing the differences in motion and flicker features between fire and other objects in videos. Second, an adaptive lightweight convolutional neural network (AL-CNN) is proposed to extract the deep static features of fire. Finally, the dynamic and static features of fire are combined to establish a video fire detection method with improved operational efficiency in terms of accuracy and run time. To prove the validity of our method, its accuracy and run time are evaluated on three test datasets, and the results reveal that our method exhibits better performance than state-of-the-art methods. Moreover, our method is shown to be feasible in complex video scenarios and for devices with resource constraints.
Keywords:
Fire detection
motion-flicker-based dynamic features
deep static features
background subtraction
flicker detection
adaptive lightweight convolutional neural network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
ShareSaveShareSaveEarly fire detection using convolutional neural networks during surveillance for effective disaster management.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
ShareSaveShareSaveComparative study of layer by layer assembled multilayer films based on graphene oxide and reduced graphene oxide on flexible polyurethane foam: flame retardant and smoke suppression propertiesRSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSaveResults of a prospective phase 2 study combining imatinib mesylate and cytarabine for the treatment of Philadelphia-positive patients with chronic myelogenous leukemia in chronic phaseBloodOAAI
BloodIF0
OAAIShareSave
View more