On using AI-Based Human Identification in Improving Surveillance System Efficiency

Alajrami, Eman; Tabash, Hani; Singer, Yassir; Astal, M.-T. El

doi:10.1109/icpet.2019.00024

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…There are several backbones that may be used for this purpose, each with its own set of benefits and downsides. The visual geometry group (VGG), (ResNet) residual neural network are among the most often used deep learning convolutional neural network (CNN) backbones in object detection techniques [30]. In terms of speed, efficiency, and accuracy, each of these designs offers various trade-offs.…”

Section: Automatic Hanggun Detection Using Machine Learningmentioning

confidence: 99%

Deep Learning Models for Crime Intention Detection Using Object Detection

Hashi¹,

Abdirahman²,

Elmi³

et al. 2023

IJACSA

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The majority of visual based surveillance applications and security systems heavily rely on object detection, which serves as a critical module. In the context of crime scene analysis, images and videos play an essential role in capturing visual documentation of a particular scene. By detecting objects associated with a specific crime, police officers are able to reconstruct a scene for subsequent analysis. Nevertheless, the task of identifying objects of interest can be highly arduous for law enforcement agencies, mainly because of the massive amount of data that must be processed. Hence, the main objective of this paper is to propose a DL-based model for detecting tracked objects such as handheld firearms and informing the authority about the threat before the incident happens. We have applied VGG-19, ResNet, and GoogleNet as our deep learning models. The experiment result shows that ResNet50 has achieved the highest average accuracy of 0.92% compared to VGG19 and GoogleNet, which have achieved 0.91% and 0.89%, respectively. Also, YOLOv6 has achieved the highest MAP and inference speed compared to the faster R-CNN.

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Section: Automatic Hanggun Detection Using Machine Learningmentioning

confidence: 99%

Deep Learning Models for Crime Intention Detection Using Object Detection

Hashi¹,

Abdirahman²,

Elmi³

et al. 2023

IJACSA

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show abstract

“…Technology has become ubiquitous in daily activities, and people use it to ensure they have topof-the-range surveillance systems. Analytics are useful in creating better surveillance while applying algorithms that enhance sensing and detection systems [44]. Machine and deep learning methods are integrated with video management systems (VMS) to augment detection solutions with artificial intelligence.…”

Section: Algorithms In Surveillance Systems For Detecting Abnormal Be...mentioning

confidence: 99%

“…GANs have various applications, such as object detection, high-quality image generation, and inpainting. Other applications include super-resolution images, person re-identification, and video prediction [44]. Thus, GANs can survey large crowds to identify abnormal behaviors.…”

Section: Generative Adversarial Network (Gan)mentioning

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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“…The classifier was trained for several images to extract facial features from video frames. However, Haar cascade is not accurate in certain environments [1].…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Camera Surveillance System with Effective Notification Features

Khodadin¹,

Pudaruth²

2020

IJCDS

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The focal point of this work is to develop an intelligent camera surveillance system which englobes the key functionalities of existing surveillance systems. Other than regular functionalities such as motion detection, object detection, face recognition and counting people, it also integrates a novel and advanced object displacement detection feature to provide more security by determining if an object has been displaced by an intruder. When people are detected, a counting module displays the number of persons present in the surveillance area. A face recognition module distinguishes between authorised and unauthorised users. This biometric functionality reduces false alarms which makes the system more robust. An object detection module detects certain valuable objects such as handbags, laptops and smartphones. Also, images and short video recordings are stored on the cloud. Furthermore, the system introduces innovative real-time notification approaches for surveillance systems such as WhatsApp messages and phone calls, in addition to SMS and emails. Thus, this system is reliable and meets the aim of a modern intelligent surveillance system by combining multiple approaches to detect intrusions and to inform users effectively.

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On using AI-Based Human Identification in Improving Surveillance System Efficiency

Cited by 14 publications

References 10 publications

Deep Learning Models for Crime Intention Detection Using Object Detection

Deep Learning Models for Crime Intention Detection Using Object Detection

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

An Intelligent Camera Surveillance System with Effective Notification Features

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