Unmasking the Abnormal Events in Videos Using Deep Learning

Surveillance systems greatly benefit from video-based anomaly detection as it helps to improve security measures and situational awareness. This research paper presents a novel methodology for anomaly detection using deep learning techniques, specifically the Long-term Recurrent Convolutional Network (LRCN). The suggested methodology integrates long short-term memory (LSTM) layers to capture temporal relationships and convolutional neural networks (CNN) to extract spatial features within video sequences. This methodology is evaluated on a diverse dataset encompassing various human activities, both normal and anomalous. Performance metrics, including False Alarm Rate, Area Under the ROC Curve (AUC), and Accuracy, demonstrate the usefulness and superiority of the suggested approach over existing methods. The results highlight the potential of this approach to enhance security and surveillance applications.