DeepGun: Deep Feature-Driven One-Class Classifier for Firearm Detection Using Visual Gun Features and Human Body Pose Estimation

• Published in: Applied sciences Vol. 15; no. 11; p. 5830
• Main Authors: Singh, Harbinder, Deniz, Oscar, Ruiz-Santaquiteria, Jesus, Muñoz, Juan D., Bueno, Gloria
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: anomaly; Datasets; Deep learning; Gun violence; human action recognition; Law enforcement; Mass murders; Neural networks; one-class classifiers; Performance evaluation; Public safety; Robbery; Surveillance; Surveillance equipment; Weapons
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15115830

The increasing frequency of mass shootings at public events and public buildings underscores the limitations of traditional surveillance systems, which rely on human operators monitoring multiple screens. Delayed response times often hinder security teams from intervening before an attack unfolds. Since firearms are rarely seen in public spaces and constitute anomalous observations, firearm detection can be considered as an anomaly detection (AD) problem, for which one-class classifiers (OCCs) are well-suited. To address this challenge, we propose a holistic firearm detection approach that integrates OCCs with visual hand-held gun features and human pose estimation (HPE). In the first stage, a variational autoencoder (VAE) learns latent representations of firearm-related instances, ensuring that the latent space is dedicated exclusively to the target class. Hand patches of variable sizes are extracted from each frame using body landmarks, dynamically adjusting based on the subject’s distance from the camera. In the second stage, a unified feature vector is generated by integrating VAE-extracted latent features with landmark-based arm positioning features. Finally, an isolation forest (IFC)-based OCC model evaluates this unified feature representation to estimate the probability that a test sample belongs to the firearm-related distribution. By utilizing skeletal representations of human actions, our approach overcomes the limitations of appearance-based gun features extracted by camera, which are often affected by background variations. Experimental results on diverse firearm datasets validate the effectiveness of our anomaly detection approach, achieving an F1-score of 86.6%, accuracy of 85.2%, precision of 95.3%, recall of 74.0%, and average precision (AP) of 83.5%. These results demonstrate the superiority of our method over traditional approaches that rely solely on visual features.