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

• 作者: Hongxin Guo Palaiahnakote Shivakumara Tong Lu Yirui Wu Yuechao He Ziming Li
• 来源期刊: 智能技术学报
• 学科: 社会科学
• 关键词: DISASTER WARNING VIDEO
• 年，卷（期）: 2019,（2）
• 页码范围: 117-121
• 页数: 5页
• 分类号: G

摘要

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.