ArticleFrontiers in big data2026
Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage.
Article in Frontiers in big data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Anomaly detection in video surveillance remains a challenging problem due to complex human behaviors, temporal variability, and limited annotated data. This study proposes an optimized spatiotemporal deep learning (DL) framework that integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for temporal dependency modeling. The CNN processes frame-level appearance information, while the LSTM captures sequential motion patterns across video frames, enabling effective representation of anomalous activities. Hyperparameter optimization and regularization strategies are employed to improve convergence stability and generalization performance. The proposed model is evaluated on the DCSASS surveillance dataset and the experimental results demonstrate that the optimized CNN-LSTM framework achieves an accuracy of 98.1%, with consistently high precision, recall, and
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