基于级联卷积神经网络的视频动态烟雾检测

复杂场景中烟雾特性的提取是目前视频烟雾检测领域的主要挑战。针对该问题，提出一种静态和动态特征结合的卷积神经网络视频烟雾检测框架。在静态单帧图像特征检测的基础上，进一步分析其时空域上的动态纹理信息以期克服复杂的环境干扰。实验结果显示，该级联卷积神经网络模型可有效应用于复杂视频场景中烟雾事件的实时检测。

Dynamic Smoke Detection Using Cascaded Convolutional Neural Network for Surveillance Videos

The extraction of stable smoke features in complex scenes is a challenging task for video based smoke detection. For this issue, a convolutional neural network (CNN) framework which employs both static and dynamic features of the smoke is proposed. On the basis of analyzing the static features of individual frame, we further explore the dynamic features in spatial-temporal domain to reduce the influence of the noise from environment. Experimental results show that the proposed cascaded convolutional neural network framework performs well in real-time video based smoke detection for complex scenes.