Academic Journal of Computing & Information Science, 2020, 3(3); doi: 10.25236/AJCIS.2020.030304.
Otabek Khudayberdiev1,*, Muhammad Hassaan Farooq Butt2
1. Sichuan Province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, PR China
2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu 61756, PR China
This paper proposes a novel approach to early fire detection system from closed-circuit television (CCTV) using combination Principal Component Analysis (PCA) and Convolutional Neural Networks (CNN). It takes full advantage of the existing traditional methods like color or motional characteristics information of fire. However, CNN based fire detection system needs more computational requirements, high memory and time, in this paper, we propose energy-friendly CNN architecture for fire detection deep neural networks, inspired by MobileNet. The main role of PCA is to perform feature extraction of row data and then send it to CNN architecture. The experimental results on benchmark fire datasets reveal that the proposed method can achieve better classification performance and indicates that using CNN to detect fire in video captures is an effective way.
Convolutional neural networks (CNN), deep learning, Principal component analysis (PCA), fire detection, surveillance networks, image classification.
Otabek Khudayberdiev, Muhammad Hassaan Farooq Butt. Fire detection in Surveillance Videos using a combination with PCA and CNN. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 3: 27-33. https://doi.org/10.25236/AJCIS.2020.030304.
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