Research on Digital Algorithms of Computer Vision Anomaly Detection for Intelligent Monitoring

This study is dedicated to the digital design and implementation of computer vision anomaly detection algorithms for intelligent monitoring. By using the generation-discrimination mechanism of GAN, an anomaly detection model is generated during the learning process of normal monitoring data, thereby improving the accuracy of abnormal event recognition. Then this study designed a detection framework based on computer vision, and achieved significant improvement in model simulation by constructing a multi-layer network structure and a dynamic discrimination mechanism. The simulation experiment used multiple public data sets and compared and analyzed them with traditional algorithms. The experimental findings indicate that the algorithm put forward in this research is better than the existing approaches in terms of the accuracy of anomaly detection and response speed. Precisely, the detection accuracy on a specific public data set attained 98.5%, and the false alarm rate was kept within 1.2%. Moreover, this article also appraised the stability of the model in diverse scenarios. The results demonstrate that the algorithm can still preserve high robustness under circumstances like complex backgrounds, occlusions and lighting changes.