Anomaly Detection in CCTV Videos Using LSTM Architecture

Abstract: The need for effective, automated anomaly detection systems that can handle enormous volumes of video data has been highlighted by the sharp rise in CCTV surveillance in both public and private areas. In order to improve situational awareness and give security personnel useful intelligence, this study proposes a model based on deep learning for identifying and measuring anomalies in CCTV data. Utilizing computer vision, the Convolutional neural networks (CNNs) are used in the model to extract spatial information. which allows it to identify and categorize anomalous occurrences in a variety of contexts. Furthermore, Long Short-Term Memory (LSTM) networks or recurrent neural networks (RNNs) are utilized to investigate mobility and activity patterns across time. This enhances the model's comprehension of sequence-based activities and enables it to identify deviations across time. Transfer learning techniques are used to improve performance even more, enabling the model to adapt effectively in a variety of settings without requiring a lot of retraining. By determining a numerical threshold score based on the anomaly's attributes, including frequency, intensity, and kind, this method is novel in that it can not only identify anomalies but also evaluate their seriousness. Because it enables security teams to rank answers based on the evaluated threat level, this severity score is essential for more efficient resource allocation and quicker reaction times.