Application of Deep Learning for Weapons Detection in Surveillance Videos

Hashmi, Tufail Sajjad Shah; Haq, Nazeef Ul; Fraz, Muhammad Moazam; Shahzad, Muhammad

doi:10.1109/icodt252288.2021.9441523

Cited by 26 publications

(9 citation statements)

References 16 publications

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“…Accuracy (mAP@0.5) YOLO v7 + Pose estimation [47] 79.66% Faster R-CNN (RegNet+) [48] 79.78% YOLO v5 (CSPDarkNet 53) [48] 77.26% YOLO v3 (DarkNet53) [49] 84% YOLO v4 [49] 85% Our proposed model 89.5%…”

Section: Model (Backbone)mentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

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Section: Model (Backbone)mentioning

confidence: 99%

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Kumar,

Shih,

Guo

et al. 2024

Future Internet

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“…Furthermore, many studies have focused on the customization of deep neural networks for the real-time detection and classification of weapons during surveillance of criminal activities. These efforts highlight the growing demand for automatic systems in policing, given the increasing rate of crime and the frequent use of handheld weapons like pistols and revolvers in illegal or criminal activities [77,[79][80][81][82][83][84][85][86]. Another study proposed a model to detect handguns based on the individual's pose, utilizing CNNs [87].…”

Section: Police Departments In Switzerland and Germanymentioning

confidence: 99%

A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

Sarzaeim,

Mahmoud,

Azim

et al. 2023

Computers

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Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language processing, have widespread applications across various fields, such as healthcare, business, and law enforcement. By means of these technologies, smart policing enables organizations to efficiently process and analyze large volumes of data. Some examples of smart policing applications are fingerprint detection, DNA matching, CCTV surveillance, and crime prediction. While artificial intelligence offers the potential to reduce human errors and biases, it is still essential to acknowledge that the algorithms reflect the data on which they are trained, which are inherently collected by human inputs. Considering the critical role of the police in ensuring public safety, the adoption of these algorithms demands careful and thoughtful implementation. This paper presents a systematic literature review focused on exploring the machine learning techniques employed by law enforcement agencies. It aims to shed light on the benefits and limitations of utilizing these techniques in smart policing and provide insights into the effectiveness and challenges associated with the integration of machine learning in law enforcement practices.

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“…Hashmi et al [36] presented a comparative analysis of YOLOV3 and YOLOV4, the two versions of object detection algorithms, for the weapons detection task. e performance of the presented work was estimated using precision, recall, quality, F1 Score, and mAP.…”

Section: Related Workmentioning

confidence: 99%

Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

Jaleel

Khurshid

Mustafa

et al. 2022

Mathematical Problems in Engineering

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Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. The traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. The purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. The goal is to alarm and alert the security officials to take proactive action to prevent violent activities. In deep learning literature, region-based classifiers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classifiers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. The method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by filtering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70–30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. This hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.

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Application of Deep Learning for Weapons Detection in Surveillance Videos

Cited by 26 publications

References 16 publications

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection

A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

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