Real-time crime monitoring system using deep learning for weapon, behavior, and facial detection

Real-time crime monitoring system using deep learning for weapon, behavior, and facial detection S. Arul Department of Computer Science, School of Computing Sciences , Vels Institute of Science, Technology & Advanced Studies (VISTAS) , Pallavaram , Chennai , India P. Kavitha Department of Computer Applications, School of Computing Sciences , Vels Institute of Science, Technology & Advanced Studies (VISTAS) , Pallavaram , Chennai , India Abstract

This study presents an efficient crime monitoring system (CMS) that leverages the UCF crime dataset, which contains actual surveillance footage of various criminal activities. The CMS integrates real-time video surveillance with deep learning (DL) models for crime detection, including weapon identification, misbehavior detection, and facial recognition. It utilizes hyper capsule networks (H-CapsNets) for identifying visible and concealed firearms, 3D convolutional neural network (C3D) for detecting anomalous behavior, and deep convolutional neural network (CNNs) for facial feature extraction and matching with a suspect database. Real-time detection is achieved with low response times—generating alerts in <1.5 s for weapon detection and 2.2 s for misbehavior detection, ensuring rapid intervention. Data augmentation techniques, including rotation, scaling, flipping, and panning, enhance model generalization, resulting in 93.5% accuracy, 92.8% precision, 91.7% recall, and 92.2% F1-score. The CMS demonstrates excellent real-time performance with low false positive (FP, 3.1%) and false negative (FN, 2.7%) rates, showcasing its potential for practical deployment in public safety systems.
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