Video smoke detection base on dense optical flow and convolutional neural network
- PDF / 3,115,363 Bytes
- 15 Pages / 439.37 x 666.142 pts Page_size
- 18 Downloads / 174 Views
Video smoke detection base on dense optical flow and convolutional neural network Yuanlu Wu 1 & Minghao Chen 1 & Yan Wo 1 & Guoqiang Han 1 Received: 19 January 2020 / Revised: 13 July 2020 / Accepted: 11 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Fire is one of the disasters with the highest probability among natural disasters and social disasters. It poses a serious threat to human life and life safety. In order to reduce fire losses, a reliable fire warning method is particularly important. But due to huge variations of smoke in color, shapes, and texture and complex application environments, the existing methods still do not meet the application requirements well. To solve these problems, in this paper, we propose a two-stage real-time video smoke detection method base on dense optical flow and convolutional neural network. In the first stage, we propose a fast pre-positioning module to obtain suspicious smoke areas through the dynamic characteristics of smoke which can greatly reduce the subsequent computational complexity, and only extract the moving optical flow of suspicious smoke areas as the dynamic features of the smoke which reduce the subsequent processing time cost. Instead of simply using moving optical flow as the dynamic characteristics of smoke, we found that the optical flow of the blue channel (OFBC) can effectively reflect the motion characteristics of smoke, so we combine the OFBC of suspicious smoke areas with its three RGB color channels to form a quaternion matrix for subsequent classification. In the second stage, we choose ResNet as our pre-classifier, and a temporal enhanced adjustment algorithm was proposed as the pre-classified follow-up fine optimization module, which can fully utilize the characteristics of the smoke movement in the video to improve detection rate. The experimental results show that compared with the existing smoke detection methods, our proposed method achieves high detection rate and low false alarm rate. Keywords Smoke detection . Convolutional neural networks . Dense optical flow
* Yan Wo [email protected]
1
College of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
Multimedia Tools and Applications
1 Introduction The purpose of real-time video smoke detection is to detect whether there is a fire from the current video surveillance area and timely warning, which plays a major role in reducing economic losses and protecting the masses. Compare with the traditional sensor detection system such as temperature, humidity, smoke, and light [10, 18], the videobased automatic detection method has the advantages of fast response, not easy to be affected by environmental factors, thus is widely used in forests, public places, and indoor areas. Due to huge variations of smoke in color, shapes, and texture and complex application environments, the field of smoke detection has attracted a lot of research efforts. In general, the goal of video smoke detection is to determin
Data Loading...
