The Identification of Anomalies in Surveillance Video using Computer Vision

Surveillance systems in the modern era heavily depend on computer vision for detecting anomalies in security footage. Real-time video feeds may be monitored and evaluated with the use of this feature, which includes object identification, motion tracking, and face detection. The system is able to distinguish between typical and suspicious actions thanks to automated anomaly detection and labeling. The technology delivers real-time warnings and messages when abnormalities are found, allowing security professionals and administrators to react quickly. In addition, the system keeps a log of past abnormalities, making it easier to conduct post-event research and gather evidence for investigations. One-Class SVM is used for supervised anomaly detection based on normal class boundaries, while Generative Adversarial Networks (GANs) are used for unsupervised anomaly detection through generated sequences, and 3D Convolutional Networks are used for spatial-temporal analysis to improve the surveillance system's anomaly detection capabilities.By integrating these computer vision methods and technologies, security systems may better protect vital regions and take a preventative approach to detecting anomalies, making the world a safer and more secure place for everyone.