Channel-wise attention model-based fire and rating level detection in video

Abstract

Due to natural disaster and global warning, one can expect unexpected fire, which causes panic among people and extent to death. To reduce the impact of fire, the authors propose a new method for predicting and rating fire in video through deep-learning models in this work such that rescue team can save lives of people. The proposed method explores a hybrid deep convolutional neural network, which involves motion detection and maximally stable extremal region for detecting and rating fire in video. Further, the authors propose to use a channel-wise attention mechanism of the deep neural network for detecting rating of fire level. Experimental results on a large dataset show the proposed method outperforms the existing methods for detecting and rating fire in video.

Keywords

• feature extraction
• learning (artificial intelligence)
• neural nets
• channel-wise attention model-based fire
• rating
• natural disaster
• global warning
• unexpected fire
• deep-learning models
• rescue team
• hybrid deep convolutional neural network
• motion detection
• maximally stable extremal region
• channel-wise attention mechanism
• deep neural network
• fire level