Human Anomaly Detection System Using YoloV8 and LSTM

The “Human Anomaly Detection System” seeks to augment real-time surveillance by pinpointing atypical human activities via a hybrid deep learning strategy that merges RGB video frame analysis with pose estimation. By employing multistream neural networks such as YOLO for object detection and MobileLSTM for the classification of temporal actions this system utilizes attention mechanisms to uncover subtle anomaliesin human behavior. The limitations of traditional surveillance methods, which usually rely on manual monitoring or rule- based frameworks that are prone to errors, are addressed by this research. Public safety, healthcare, and educational institu- tions can all benefit from the system’s notable improvements in recognizing complex, non-rigid human behaviors, despite the difficulties in combining spatial and temporal data. This technology is positioned as a powerful solution for anomaly identification in dynamic contexts since evaluation results showa high degree of accuracy and dependability. Future work will focus on improving model modularity and scalability to enable wider hardware flexibility and investigating richer datasets to improve performance across a range of scenarios.