Smoke Detection in Video for Early Warning Using Static and Dynamic Features

Abstract

An image processing approach for detection of smoke in video using static and dynamic features is proposed in this paper. As the conventional smoke detection such as particle sampling, smoke, temperature, Humidity Sensors needs to be close to the source of the smoke for detection, also they have weakness in wide coverage area and low response time. In order to overcome these shortcomings, this paper presents a method based on image processing techniques, capable to identify smoke from video taken from video dataset. The proposed detection method consists of the following steps: moving region detection, smoke color detection, detecting smoke disorder, direction and then classification phase. This will provide early warnings such as fire, thus reducing economic losses and casualties. In addition, it will help to improve the rate of smoke detection, as well as reducing the false detection rate of other suspected object. The paper is structured as follows: section 1 describes introduction part and features of smoke used to differentiate it from other suspected object. Then section 2 describes the previous work including conventional and recent research on smoke detection. Next, section 3 describes the proposed smoke detection system. Finally conclusion is given.