Intelligent Frame Retention and Anomaly Detection with Notification Using YOLOv11m

This study aims to solve the problem of video storage and improves the overall efficiency of cameras by adopting real-time anomaly detection, hence informing the user about any suspicious anomalies. The proposed trained model processes live video streams, identifying unusual events and anomalies such as theft, weapons, or violent activities. Simultaneously, a video storage optimization algorithm reduces redundant frames while maintaining movement detected video streams from CCTV surveillance. In addition, if the model detects an unusual event occurring in the live video stream, it immediately notifies the user about the type of anomaly and the location of the event that occurred. Experimental results demonstrate that the proposed system effectively detects anomalies with an average precision–recall score of 0.958 and an F1 confidence score of 0.93, ensuring reliable threat identification and detection. The model is robust and differentiates between normal and anomalous activities as justified by experimental results.