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Video Smoke Detection For Surveillance Cameras Based On Deep Learning In Indoor Environment

Nguyen, Viet Thang and Quach, Cong Hoang and Pham, Minh Trien (2020) Video Smoke Detection For Surveillance Cameras Based On Deep Learning In Indoor Environment. In: The 4th International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom2020), August 28th – 29th, 2020, Hanoi, Vietnam. (In Press)

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An early fire detection in indoor environment is essential for people’s safety. During the past few years, many approaches using image processing and computer vision techniques were proposed. However, it is still a challenging task for application of video smoke detection in indoor environment, because the limitations of data for training and lack of efficient algorithms. The purpose of this paper is to present a new smoke detection method by using surveillance cameras. The proposed method is composed of two stages. In the first stage, motion regions between consecutive frames are located by using optical flow. In the second stage, a deep convolutional neural network is used to detect smoke in motion regions. To overcome the problem of lacking data, simulated smoke images are used to enrich the dataset. The proposed method is tested on our data set and real video sequences. Experiments show that the new method is successfully applied to various indoor smoke videos and significant for improving the accuracy of fire smoke detection. Source code and the dataset have been made available online

Item Type: Conference or Workshop Item (Lecture)
Subjects: Electronics and Communications
Divisions: Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr Minh Trien Pham
Date Deposited: 18 Jul 2020 02:45
Last Modified: 18 Jul 2020 02:45

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