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Vanesa Lomas
Department of Electrical, Electronic, Automatic and Communications Engineering—IEEAC, Higher Technical School of Industrial Engineering, University of Castilla-La Mancha, Avenida de Camilo José Cela s/n, 13071 Ciudad Real, Spain

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Journal article
Published: 30 June 2021 in Applied Sciences
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There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training.

ACS Style

Jesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences 2021, 11, 6085 .

AMA Style

Jesus Salido, Vanesa Lomas, Jesus Ruiz-Santaquiteria, Oscar Deniz. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences. 2021; 11 (13):6085.

Chicago/Turabian Style

Jesus Salido; Vanesa Lomas; Jesus Ruiz-Santaquiteria; Oscar Deniz. 2021. "Automatic Handgun Detection with Deep Learning in Video Surveillance Images." Applied Sciences 11, no. 13: 6085.