Improving handgun detection through a combination of visual features and body pose-based data

Ruíz-Santaquiteria, Jesús; Velasco-Mata, Alberto; Vállez, Noelia; Déniz, Óscar; Bueno, Gloria

doi:10.1016/j.patcog.2022.109252

Cited by 9 publications

(3 citation statements)

References 10 publications

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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

Data in Brief

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Section: Data Descriptionmentioning

confidence: 99%

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

Ruiz-Santaquiteria,

Muñoz,

Maigler

et al. 2024

Data in Brief

Self Cite

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“…Currently, most systems require, each operator must control the video sequences of multiple security cameras simultaneously [26]. This fact, along with inherent human characteristics such as fatigue or loss of attention over time, can lead to ignoring important events or responding too late, causing significant damage and making these video surveillance systems inefficient or useless [27].…”

Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.

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“…Further improvements are needed to optimize its performance in dynamic scenarios and mitigate limitations related to occlusions and varying lighting conditions. The author in [19] focused on enhancing handgun detection by combining visual features with body pose-based data. The method involves extracting both appearance-based features and human body joint positions from images.…”

Section: Related Workmentioning

confidence: 99%

An Edge Computing-based Handgun and Knife Detection Method in IoT Video Surveillance Systems

Liu,

2023

IJACSA

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Real-time handgun and knife detection on edge devices within the Internet of Things (IoT) video surveillance systems hold paramount importance in ensuring public safety and security. Numerous methods have been explored for handgun and knife detection in video-based surveillance systems, with deep learning-based approaches demonstrating superior accuracy compared to other methods. However, the current research challenge lies in achieving high accuracy rates while managing the computational demands to meet real-time requirements. This paper proposes a solution by introducing a single-stage convolutional neural network (CNN) model tailored to address this challenge. The proposed method is developed using a custom dataset, encompassing model generation, training, validation, and testing phases. Extensive experiments and performance evaluations substantiate the efficacy of the proposed approach, which achieves remarkable accuracy results, thus showcasing its potential for enhancing real-time handgun and knife and knife detection capabilities in IoT-based video surveillance systems.

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Improving handgun detection through a combination of visual features and body pose-based data

Cited by 9 publications

References 10 publications

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

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

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

An Edge Computing-based Handgun and Knife Detection Method in IoT Video Surveillance Systems

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