Abnormal Activity Detection Using Video Action Recognition: A Review

The escalating ubiquity of video surveillance systems in both public and private sectors underscores an urgent need for automated mechanisms capable of identifying anomalous behaviors. Traditional methods, largely heuristic in nature, are increasingly being supplanted by neural network-based approaches, offering a more nuanced and effective means for anomaly detection in video data. While Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been extensively investigated, the incipient exploration of Transformer models in this domain represents a compelling research frontier. Transformer models, while beneficial, face challenges like computational intensity and data requirements. This paper presets the comparative analysis of deep learning for Abnormal activity detection. In our study, we assess the strengths and weaknesses of widely-used methods, considering both their structural features and their practical effectiveness in experiments. Additionally, we present an examination of emerging research avenues and potential future endeavors. Future directions should focus on optimizing neural architectures and transformers for real-time analytics, addressing skewed data, and establishing ethical guidelines for automated surveillance. It calls for interdisciplinary studies on the ethical implications of automated anomaly detection, setting the stage for enhancing Transformer models in real-time surveillance and evaluating their resilience against adversarial attacks.