The Target Detection Optimization And Tracking Analysis Of Intelligent Video Monitoring

With the rapid development of the artificial intelligence and machine learning,the development of intelligent monitoring systems is increasingly faster and its extensive applications involving aerospace remote sensing,bio-medical,industrial automation,intelligent robots,Military public security objective investigation,even a multimedia television production of the arts.Especially,with the growing demand for security monitoring,using only human for thousands of surveillance cameras online monitoring and massive surveillance video analysis has been unable to meet demand.The camera can automatically analyze the collected images in intelligent security monitoring without the need for human intervention has become a hot research.In this paper,the relevant key technology of intelligent security monitoring systems were discussed and studied under the application environment within single webcam and a fixed lens case.Moving target detection is the first step in intelligent security monitoring system implementation,its test results directly affect the moving object classification and behavior analysis.In this paper,the background subtraction method based on Gaussian mixture model is used for moving target detection.An improved method for block-based thinking is presented to solving computationally intensive processing speed which can not meet the real-time monitoring.This improved algorithm improves the operational speed to meet the requirements of real-time processing under the premise of good testing quality.Target classification is a hot research topic of intelligent security monitoring.The moving objects in the non-motor vehicles parking is divided into three categories: people,bicycles and people.This article has extracted features like Hu invariant moments,aspect ratio,duty cycle,tightness,eccentricity.In this paper,commonly used classification methods are analyzed and compared in consideration of the real-time monitoring of the premise.Finally,the ELM algorithm with faster computing and higher precision is chosen.Moving target tracking is a key part for implement the “intelligence”.Because this study is under fixed scene application environment,the tracking algorithm based on Kalman algorithm is chosen which can make predictions for the next motion information based on the existing state of motion of the moving object.It is fast and efficient for tracking moving targets and can effectively solve the target occlusion problems.