Channel‐wise attention model‐based fire and rating level detection in video. Issue 2 (31st May 2019) - Wu, Yirui; He, Yuechao; Shivakumara, Palaiahnakote; Li, Ziming; Guo, Hongxin; Lu, Tong
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.