Research On Population Density And Population Estimates Based On Intelligent Video Surveillance

In recent years,with the rapid development of social economy and science and technology,intelligent video surveillance has gradually entered people's daily lives.This is a product of social development,and it is also the need to build safe cities and smart cities.Therefore,it is very necessary and important to carry out crowd density prediction and monitoring of people flow in the public places,especially at the famous tourist spots,stations,squares,and shopping malls.Intelligent video surveillance has become one of the hot areas researched by related scholars at home and abroad.It has broad application prospects.By estimating the density of people in the monitoring scene,it can not only grasp the scene traffic in a timely and effective way,but also can be used in intelligent monitoring systems.The performance was improved and made an extremely important contribution to the security.This article first introduced the research status of population density estimation at home and abroad,and fully explained and elaborated the contents of intelligent monitoring technology,image preprocessing technology and foreground target detection,and researched and analyzed the current mainstream crowd density estimation methods,mainly including Population-based population estimation methods and population-based population estimation methods.The analysis shows that the population statistics estimation method based on pixel statistics is simple to implement and has low complexity.It has good statistical results for low-density scene populations and is not suitable for high-density scene crowds.On the contrary,the crowd estimation method based on texture analysis is used to calculate Due to its complexity and high complexity,it can achieve good results for crowds with higher density scenes,but it is not ideal for low-density crowd scenes.Based on the existing methods of crowd estimation,this paper analyzes the related issues and applies them innovatively.Combined with the actual situation in the monitoring scene,the current method of crowd statistics based on video surveillance is improved.A method based on linear regression of adaptive correction coefficients is proposed to realize the statistics of the number of people in the video surveillance system.The method detects all corner points in the video frame by the Harris corner detection algorithm,and then uses the Lucas-Kanade optical flow method to filter the static corner points generated by the background information,thereby only obtainingthe moving corner from the moving crowd in the video sequence.According to the static linear proportional relationship between the number of people's corners and the number of people in the image sequence proposed by Albiol,we improved the Albiol's method,in other words,to find a self-adaptively modified dynamic proportional coefficient and combine the adaptive correction coefficient Albiol's method to achieve the statistics of the crowd in the video frame,and the relevant experiments have been done to verify.We took the average relative error and the average absolute error to evaluate and analyze it.Compared with Albiol's method,this method can obtain more accurate results.