| 摘要 |
An all-weather video monitoring method based on deep learning. The method includes the following steps: collecting a video stream in real time, and obtaining multiple original sampling graph samples and speed sampling graph samples through line sampling on the basis of the obtained video stream; carrying out space-time correction on the obtained speed sampling graph samples; on the basis of original sampling graphs and speed sampling graphs, carrying out off-line training to obtain a deep learning model, wherein the deep learning model includes a classification model and a counting model; and carrying out a crowd state analysis on the real-time video stream by means of the obtained deep learning model. The method is well adaptive to different environments, illumination intensities, weather situations and camera angles. Higher accuracy can be guaranteed in terms of crowding environments, such as rushing out of mass flow crowds. A calculated amount is small, requirements for real-time video processing can be met, and the method can be widely applied to monitoring and management of public places, such as buses, subways and squares where stranded crowds are dense. |
