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Home intrusion: smart security and live video surveillance system

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Authors:
Punith M S
Punith M S
Punith M S
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Mamatha Inna at Amrita Vishwa Vidyapeetham
Mamatha Inna
Shikha Tripathi
Shikha Tripathi
Shikha Tripathi
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Abstract and Figures

Security concerns are rampant in our world today. No location is immune to crime, whether it be public spaces, commercial areas, offices, or private property. While law enforcement agencies work to address crimes after they have taken place, there is no guarantee that they can prevent them from occurring. Communities and businesses often turn to security personnel and surveillance cameras to keep watch, but this only provides passive protection. Criminals may act while security is absent and cameras only record events, they do not prevent them. Although, there are few systems with face recognition and alerting mechanisms are existing, they are too expensive and not affordable to a common man. In response to this challenge, this work proposes a most economic, hassle free proactive home security system which can be used as a plug and play device for the already installed surveillance system employing CCTV cameras. This system features facial recognition technology, enabling it to identify authorized individuals by storing their images in a database, thus swiftly distinguishing between intruders and residents. In cases of unauthorized intrusions, the system promptly activates alerts and alarms. Additionally, it incorporates supplementary security measures utilizing sensors and actuators for heightened protection. Moreover, the system utilizes an IP camera, permitting remote monitoring via live streaming. An array of machine learning algorithms is assessed to ascertain the most effective method for facial recognition, integrated into a monitoring dashboard using RTSP streaming.
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Eng. Res. Express 6(2024)045370 https://doi.org/10.1088/2631-8695/ad9fd7
PAPER
Home intrusion: smart security and live video surveillance system
Punith M S
1
, Mamatha I
2,∗
and Shikha Tripathi
3
1
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Bengaluru,
Karnataka-560035, India
2
Department of AI & ML Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Vishwothama Nagar, Bantakal,
574115, Udupi District, Karnataka, India
3
Department of Electronics & Communication Engineering, PES University, Bangalore, Karnataka-560085, India
∗
Author to whom any correspondence should be addressed.
E-mail: punithms17@gmail.com,mamraj78@gmail.com and shikha.eee@gmail.com
Keywords: CCTV, RTSP, IP camera, face recognition
Abstract
Security concerns are rampant in our world today. No location is immune to crime, whether it be
public spaces, commercial areas, offices, or private property. While law enforcement agencies work to
address crimes after they have taken place, there is no guarantee that they can prevent them from
occurring. Communities and businesses often turn to security personnel and surveillance cameras to
keep watch, but this only provides passive protection. Criminals may act while security is absent and
cameras only record events, they do not prevent them. Although, there are few systems with face
recognition and alerting mechanisms are existing, they are too expensive and not affordable to a
common man. In response to this challenge, this work proposes a most economic, hassle free
proactive home security system which can be used as a plug and play device for the already installed
surveillance system employing CCTV cameras. This system features facial recognition technology,
enabling it to identify authorized individuals by storing their images in a database, thus swiftly
distinguishing between intruders and residents. In cases of unauthorized intrusions, the system
promptly activates alerts and alarms. Additionally, it incorporates supplementary security measures
utilizing sensors and actuators for heightened protection. Moreover, the system utilizes an IP camera,
permitting remote monitoring via live streaming. An array of machine learning algorithms is assessed
to ascertain the most effective method for facial recognition, integrated into a monitoring dashboard
using RTSP streaming.
1. Introduction
The rapid advancement of technology has brought about a shift towards automation, particularly in areas like
robotics, building management, drone surveillance, and self-driving cars. As people seek a more convenient
lifestyle, they have become increasingly dependent on technology. This is evident in the growing trend of home
surveillance systems that rely on machines rather than human intervention. Thanks to the abundance of data
and computing power, these systems are now able to make informed decisions and analyze their surroundings.
However, the rise of technology has also led to an increase in crime, including home theft and security
breaches. Historically, security was maintained through the use of guards or watchmen, but these individuals are
prone to factors such as fatigue, illness, and sleep deprivation that can affect their performance. Corporate
surveillance typically involves the use of CCTV cameras, which can provide footage for post-incident analysis
but not real-time response. To address these security concerns, we propose the implementation of an intelligent
system that uses artificial intelligence (AI)to enhance the capabilities of CCTV cameras. AI has proven to be an
effective tool in the security industry and has advanced to the point where it can perform tasks such as human
recognition and detection. This work will leverage AI algorithms and services to create a human face detection
and recognition model that can trigger alerts and alarms in response to intruders.
RECEIVED
9 August 2024
REVISED
5 December 2024
ACCEPTED FOR PUBLICATION
16 December 2024
PUBLISHED
30 December 2024
© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
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