Automated Video Anomaly Detection (VAD) plays a vital role in developing surveillance systems in public spots. Our study develops real-time anomaly detection via a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, which uses the UCSD Pedestrian (Ped2) dataset. It introduces a methodology designed for detection accuracy enhancements by extracting CNN-based spatial features combined with learning LSTM-based temporal sequences. Preprocessing manages the class imbalance issue throughout several phases, including frame extraction, resizing, normalization, augmentation, and SMOTE balancing. Regarding the evaluation phase, several metrics such as accuracy, precision, recall, F1-score, and AUC are applied, indicating the superior performance of the CNN-LSTM model, which could outperform both the standalone CNN and LSTM models, having 93.5% accuracy, 91.8% precision, 90.2% recall, 91.0% F1-score, and an AUC of 0.947. Conclusively, our methodology is designed for improving the accuracy of the detection phase by integrating CNN-based spatial feature extraction along with LSTM-based temporal sequence learning.
Mousa et al. (Thu,) studied this question.