Human Abnormal Activity Prediction of Long Short-Term Memory using CNNs and RNNs in Deep Learning

This research presents a unique method of predicting aberrant activity in humans by integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN). By using the advantages of CNNs for frame-by-frame spatial analysis and LSTM networks for temporal dependency modeling inside the frame sequence, our hybrid model seeks to efficiently capture both spatial and temporal characteristics in video sequences. Combining these two designs, we get a strong model that can identify complex patterns suggestive of anomalous human behavior. The CNN LSTM hybrid model that has been suggested performs better in terms of accuracy and resilience when predicting aberrant behaviors than conventional approaches and standalone models, as shown by experimental validation on a variety of benchmark datasets. The integration of both spatial and temporal information advances our knowledge of the dynamics at play in the identification of aberrant behavior. Furthermore, our approach is flexible enough to be used to real-world surveillance situations, demonstrating encouraging outcomes in scenarios with different camera viewpoints and environmental circumstances. The use of CNN and LSTM not only improves prediction accuracy but also makes the model more interpretable by shedding light on the spatiotemporal characteristics of aberrant behaviors. To sum up, this study advances the field of intelligent video analysis by providing a useful tool for predicting anomalous human behavior. This tool has potential uses in security monitoring, public safety, and anomaly detection systems.