Efficient Anomaly Detection in Surveillance Videos Using Pre-Trained CNNs and Self-Supervised Learning

Abstract

The fast growth of surveillance systems in both public and non-public spaces has led to the creation of large amounts of video data, whose manual surveillance becomes inefficient and unreliable. Automated video anomaly detection has thus evolved to be an important topic area in computer vision and that seeks to detect rare, unexpected, and even dangerous events in real time. Nevertheless, most of the current techniques use labelled anomaly data or computationally complex architectures, so they have a limited generalization capability and can only be used in real-world surveillance settings. This thesis presents Efficient-VAD, a self-supervised video anomaly detection model that can reach high accuracy and yet is a real-time model. The suggested system uses the pretrained MobileNetV3-Large network to extract spatial features efficiently and a memory-augmented LSTM autoencoder to classify normal behavioral patterns of space and time using video data that is unlabeled. The distinctiveness of abnormal occurrences by observing patterns that do not conform to the usual representations of normal objects leads to the detection of the abnormal objects by the framework by raising reconstruction errors in cases where abnormal objects are observed to deviate. The comprehensive experiments of the UCF-Crime dataset prove the efficiency of the suggested strategy, with an AUC-ROC of 0.9861 in the severe conditions of class imbalance. Moreover, Efficient-VAD is applicable to real-time and resource-intensive surveillance environments with a low 512 MB memory footprint and a rate of 87.51 frames per second. These findings confirm that the integration of self-supervised learning and memory-enhanced temporal model is effectiv