Human Abnormal Activity Detection Using CNN and LSTM

The detection of human abnormal activities plays a crucial role in numerous applications, including security surveillance and healthcare monitoring. In this study, a novel approach is proposed for human abnormal activity detection using 3DCNN and LSTM. The proposed method leverages the spatial and temporal relationship within the video sequences to accurately identify abnormal activities. Firstly, the video frames are extracted and transformed into volumetric representations to capture spatial information. Then, a CNN model is employed to learn informative spatiotemporal features from the video volumes. The learned features are subsequently fed into an LSTM network to model temporal dependencies. This enables the system to effectively capture the dynamic nature of human activities. To facilitate timely response, an email and beep alert mechanism is designed to notify the responsible parties in case of any detected abnormal activity. This ensures that immediate actions can be taken to mitigate potential threats or provide timely assistance. The proposed approach is validated on benchmark datasets and achieves promising results, outperforming existing methods. The combination of CNN and LSTM proves to be effective in accurately capturing human activities and detecting abnormalities. The integration of email and beep alerts further enhances the practicality of the system, enabling real-time monitoring and incident response. Overall, this research contributes to the development of intelligent surveillance systems for various applications where abnormal activity detection is critical.