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)
Abstract
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
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