Advanced Deep Learning Techniques for Detecting Anomalous Human Behavior Patterns

The proliferation of security surveillance setups in both public and private spaces has made the need for advanced video analysis to have a clear edge in security and public safety. Detecting the abnormal human behavior on video streams, specifically in crowded locations due to the rare occurrence and unpredictable nature of such events (which usually appear as spatiotemporal deviation from normal activity), presents a challenging task. This paper proposes a deep learning based framework for a real-time abnormal behavior detection using a hybrid framework that combines 3D Convolutional Neural Networks and Temporal Transformers. The 3D CNN module captures the local spatiotemporal features between subsequent frames whereas the Temporal Transformer links the long-range temporal features to identify the minute behavioural anomalies. Compared to the classical object detection methods like YOLO, which mainly focuses on spatial characteristics, the above scheme can focus more on temporal modeling to accurately identify such complex and delayed abnormal behaviors as theft, attack, and traffic violation. Despite the continuous challenge in camera placement and scalability of this system, this paper demonstrate the effectiveness of this framework as the means to enable automated and context-aware anomaly detection for use in increasing public safety for video surveillance applications.