Spatiotemporal deep learning framework for predictive behavioral threat detection in surveillance footage

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Anomaly detection in video surveillance is difficult due to complex behaviors and limited data.
• Existing methods struggle with temporal variability and accurate feature representation.

Purpose of the Study:

• To develop an optimized spatiotemporal deep learning framework for enhanced video surveillance anomaly detection.
• To improve the accuracy and robustness of identifying anomalous activities in surveillance footage.

Main Methods:

• Integration of a Convolutional Neural Network (CNN) for spatial feature extraction.
• Utilization of a Long Short-Term Memory (LSTM) network for modeling temporal dependencies.
• Application of hyperparameter optimization and regularization for improved performance.

Main Results:

• The optimized CNN-LSTM framework achieved 98.1% accuracy on the DCSASS dataset.
• High precision, recall, and F1-score were consistently observed across various cross-validation folds.
• The proposed model outperformed conventional and recent deep learning approaches.

Conclusions:

• The developed CNN-LSTM framework offers an effective and robust solution for video-based anomaly detection in surveillance.
• The study highlights the potential of optimized deep learning for real-world security applications.
• Further research can explore scalability and real-time implementation of this framework.