Video smoke detection base on dense optical flow and convolutional neural network

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.