Design and Application of Intelligent Community Policing Security System based on OC-SVM Algorithm

With the rapid development of smart communities, the volume of data in the community policing security system is increasing, which poses new challenges to behavioral anomaly monitoring. This paper proposes a behavioral anomaly monitoring method based on the One-Class Support Vector Machine (OC-SVM) algorithm and the MobileNetV3 improved OpenPose model. The fundamental theories of human posture recognition and behavioral anomaly detection are first reviewed, followed by a discussion of the limitations of the traditional OpenPose model. The structure of the MobileNetV3 lightweight network and its application advantages in human posture detection are described in detail. In this study, the feature extraction process was optimized using parallel computing technology to address the shortcomings of the OpenPose model, and an efficient method for monitoring behavioral anomalies was proposed. The test results of the system on the Multiple Cameras Fall dataset showed that the proposed method achieved 86.9% and 84.3% precision and recall for fall detection, respectively, showing significantly improved detection performance compared to the traditional method. The algorithm improvement in this paper provides a new solution for police security monitoring in smart communities, ensuring the safety and wellbeing of community residents.