Real-Time Handgun Detection in Surveillance Videos based on Deep Learning Approach

T, Pavithra; Thangaraj, Rajasekaran; Pandiyan, P.; M, Uma Rani; Vadivelu, Balasubramaniam

doi:10.1109/icaaic53929.2022.9793288

Cited by 3 publications

(1 citation statement)

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“…Despite the advancements, there exist certain limitations and research challenges in the realm of deep learning-based approaches for handgun detection [14]. Pursuing high accuracy while maintaining real-time performance demands innovative solutions [15,16]. Addressing these challenges necessitates further investigation and exploration of novel methodologies to ensure the efficacy of real-time handgun detection systems.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%