%0 Conference Paper
%A Nguyen, Viet Thang
%A Quach, Cong Hoang
%A Pham, Minh Trien
%B The 4th International Conference on Recent Advances in  Signal Processing, Telecommunications & Computing (SigTelCom2020)
%C Hanoi, Vietnam
%D 2020
%F SisLab:4021
%T Video Smoke Detection For Surveillance Cameras Based On Deep Learning In Indoor Environment
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4021/
%X 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