关键词: early smoke, color judgment, mean HASH algorithm, multi-scale 3D convolutional neural network

Abstract:

To solve the problem of high false alarm and high missed detection in the complex environment of early smoke detection based on video, a method based on motion extraction of suspected areas is proposed and a multi-scale 3D convolutional neural network with input of 6 frames（6M3DC） is designed for video smoke detection. Firstly, the motion regions are obtained through the background difference model after average filtering and the positions of the block in which the motion regions are located are calculated, and then the motion blocks are extracted by color judgment and mean HASH algorithm and the nonconforming blocks are updated to the background image. Finally, by combining the suspected blocks of the same region of 6 consecutive frames as the input for the 3D convolutional neural network for detection, blocks detected as smoke are marked and non-smoke blocks are updated to the background image. The experimental results show that the algorithm is adaptive to slow moving smoke and can detect smoke in complex environment.

Key words: early smoke, color judgment, mean HASH algorithm, multi-scale 3D convolutional neural network