Design And Optimization Of Intelligent Video Surveillance Security System For Residential Area Based On Machine Learning

With the continuous improvement of living standards,people’s requirements for the safety of residential areas are getting higher and higher.However,as video resolution improves and the number of monitoring cameras increases rapidly,the size of stored video data also expands exponentially,seriously shortening the storage time of videos and reducing the efficiency of daily data retrieval and review.A reliable system can not only improve the level of intelligence but also significantly increase work efficiency while saving labor costs.In this context,it is particularly important to replace some repetitive labor behaviors with machines.In light of the above problems,this paper proposes a small-scale community video surveillance and security system based on machine learning.Specifically,the system includes traditional real-time video monitoring and storage functions,highperformance video compression based on the new generation video codec H.266/VVC,and intelligent selective storage of videos based on anomaly event-driven multi-point correlation.With limited hardware resources,the intelligent video surveillance system for communities has been designed and implemented.Through experimental verification,the system can detect and alert abnormal behaviors,greatly improve the storage capacity of monitoring video data,and in real life,its application value is very/quite good.The following is the specific results of the article.(1)In response to the problem of excessive memory resources occupied by community monitoring videos,the H.266/VVC video coding standard is adopted for transmission and storage,and a frame-level rapid CU partition decision algorithm based on the random forest classifier is designed to further increase the video coding efficiency.(2)To address the challenges of storing and searching large-scale video data,a smart storage algorithm based on abnormal event detection is proposed.A model is established based on actual situations and appropriate storage strategies are selected to achieve selective intelligent storage of surveillance videos.Corresponding labels are added to the video segments,making it easier to filter out key information during manual browsing and retrieval.(3)To address the limitations of anomaly events,a multi-level architecture design is adopted to avoid coupling between different modules,facilitating the addition and removal of anomaly event analysis algorithms and increasing the system’s scalability.(4)Based on the analysis of requirements for a real community,a video surveillance and security system is designed and constructed,with hardware and software architecture,and through experimental testing,the system’s various performance indicators are verified,demonstrating its high practical value in actual applications.