Automatic Handgun Detection with Deep Learning in Video Surveillance Images

Jesus, S. M.; Lomas, Vanesa; Ruíz-Santaquiteria, Jesús; Deniz, Oscar

doi:10.3390/app11136085

Cited by 31 publications

(9 citation statements)

References 38 publications

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“…The usage of transfer learning and deep learning techniques effectively improves the over-all detection speed and accuracy. Similar to the prior literature, Salido [17] integrated YOLO V3, RetinaNet, and faster R-CNN models for an effective handgun detection in the video surveillance images. As mentioned earlier, the computational complexity of the hybrid deep learning model was higher compared to existing machine learning methods.…”

Section: Related Workmentioning

confidence: 87%

Weapon Detection in Surveillance Videos Using Human Inspired Particle Swarm Optimization Algorithm and Support Vector Machine

2022

IJIES

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In recent decades, automatic control systems are becoming the essential need for security forces, due to the increase in the number of criminal activities. The fast and precise automatic weapon detection system is useful to mitigate or avoid risks in public spaces. In this manuscript, a new automated model is implemented for effective weapon detection in closed circuit television videos. After collecting the data from YouTube and Gun movies databases, the Gaussian Mixture Model (GMM) is applied to detect the weapons in the video sequences. Then, the feature extraction is performed using deep learning models: AlexNet and ResNet 18, and a descriptor: Scale Invariant Feature Transform (SIFT) for extracting the feature vectors from the segmented regions. Whereas, the combination of deep and texture features reduces the semantic space between the feature sub-sets that helps in enhancing the classification performance. In addition, the feature optimization is accomplished by Human Inspired Particle Swarm Optimization (HIPSO) algorithm to select active feature vectors that decrease the system complexity and training time of the classifier. In the conventional PSO algorithm, the Human Group Optimization (HGO) algorithm is utilized to influence the particles, and then the adaptive uniform mutation is utilized to improve the convergence rate and makes the implementation simple. Finally, the selected active feature vectors are fed to the Support Vector Machine (SVM) classifier for weapon and non-weapon classification. The experiment results confirmed that the HIPSO-SVM model has achieved high accuracy of 95.34% and 98.60% on the YouTube and Gun movies databases, which are better compared to the existing models.

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Section: Related Workmentioning

confidence: 87%

Weapon Detection in Surveillance Videos Using Human Inspired Particle Swarm Optimization Algorithm and Support Vector Machine

2022

IJIES

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“…In that sense, we consider that extending the CCTV to avoid blind spots and modifying the infrastructure so that the images are not only used for a posteriori viewing but also for real-time analysis would allow a control of these events without the need to involve every single occupant of the building. Future work has been noted in [10]- [12] where pose and video analysis is used to improve the accuracy of detection of weapons and violent acts.…”

Section: Discussionmentioning

confidence: 99%

Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

Álvarez-García

González

Deniz

et al. 2021

2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)

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This paper describes the progress and work carried out during the execution of the VICTORY project (Vision and Crowdsensing Technology for an Optimal Response in Security). It describes both its motivation and the progress made in the field of managing violent attacks in buildings. It is assumed that these buildings have a WiFi infrastructure that allows indoor localisation through mobile applications and a closed-circuit television system that allows real-time images to be captured. The advances of this project have allowed 1) the design of a crowdsensing application that allows indoor localisation in a reliable way and with a low battery consumption as well as the detection of stampedes and falls; 2) the development of a multi-agent model that simulates human behaviour in the face of a violent attack and 3) new approaches to improve the state of the art in computer vision for the detection of weapons.

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“…The timely identification of hazardous objects, such as firearms, within images is of utmost importance in mitigating potential harm [6] , [7] , [8] , [9] . The dataset presented herein offers a comprehensive assortment of authentic surveillance footage, thereby facilitating the advancement and evaluation of machine learning models within these domains.…”

Section: Data Descriptionmentioning

confidence: 99%

Firearm-related action recognition and object detection dataset for video surveillance systems

Ruiz-Santaquiteria,

Muñoz,

Maigler

et al. 2024

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Automatic Handgun Detection with Deep Learning in Video Surveillance Images

Cited by 31 publications

References 38 publications

Weapon Detection in Surveillance Videos Using Human Inspired Particle Swarm Optimization Algorithm and Support Vector Machine

Weapon Detection in Surveillance Videos Using Human Inspired Particle Swarm Optimization Algorithm and Support Vector Machine

Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

Firearm-related action recognition and object detection dataset for video surveillance systems

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