Intelligent Integrated Home Security System Using Raspberry Pi

Author(s):  
Pallavi Mitra

Security, be it of a small apartment or of a sophisticated, gigantic institute is of arrant concern. In metro cities in India, for a housing complex/small apartment, security personnel are generally employed for the said purpose, who may not be that efficient especially at night. This paper intends to build an “Intelligent Home Security System” based on Digital Image Processing and Speech Processing, using a Raspberry Pi. The system is divided into two sub- systems. 1. Allowing/Disallowing vehicles based on Number Plate Recognition2. Allowing/Disallowing human beings based on Face Detection and  Recognition and Speech RecognitionA database of the residents of the building is prepared. It consists of a pre-recorded security code word and an image of the resident. A separate vehicular database containing the number plates of the cars is also stored in the memory. 

Author(s):  
Wahyuni Kurniasih ◽  
Abdul Rakhman ◽  
Irma Salamah

The house is the most valuable asset, therefore security at home is also very important. Therefore a home security system is created that combines a microcontroller with an Android smartphone application. The microcontroller used is the Raspberry Pi which is equipped with a camera as a home security monitoring system and various sensors as detectors such as magnetic, PIR sensors and solenoids as automatic door locks. So if the sensors that are installed detect something at home, then the homeowner will immediately get a notification sent by the database to the smartphone application, and the homeowner can monitor the state of the house right then through photos and videos recorded by cameras that have been installed at home.


2020 ◽  
Vol 176 (13) ◽  
pp. 45-47
Author(s):  
Manoj R. ◽  
Rekha Y. ◽  
Raju R. ◽  
Sharad A.

2021 ◽  
Author(s):  
Indhuja G ◽  
Aashika V ◽  
Anusha K ◽  
Dhivya S ◽  
Meha Soman S

In the present world the security of the home, banks, shops, etc., are the prime concerns. The traditional security such as Closed-Circuit Television (CCTV) cameras are very easy to break and lead to theft. And moreover, the installation cost of the security systems is costlier. To overcome these problems, we are presenting Internet of Things (IoT) based solution where we can setup a smart security system. In this paper, we are proposing the system with the help of face detection and face recognition algorithms to secure our home which gives us the facility of entire surveillance of our buildings remotely and take appropriate action if anything goes wrong. The Camera Serial Interface (CSI) is attached to the Raspberry PI which detects presence of person using Face detection and recognition algorithms. The multiple Raspberry PIs attached in different areas of our buildings are connected to the main Raspberry PI which acts as hub module. If the person is identified as unknown, the information is sent to Hub module which in turn sends the alert message and live video streaming to the user using an app which we developed.


Author(s):  
Syafeeza Ahmad Radzi ◽  
M.K. Mohd Fitri Alif ◽  
Y. Nursyifaa Athirah ◽  
A. S. Jaafar ◽  
A. H. Norihan ◽  
...  

The home security system has become vital for every house. Previously, most doors can be open by using traditional ways, such as keys, security cards, password or pattern. However, incidents such as a key loss has led to much worrying cases such as robbery and identity fraud. This has become a significant issue. To overcome this problem, face recognition using deep learning technique was introduced and Internet of Thing (IoT) also been used to perform efficient door access control system. Raspberry Pi is a programmable small computer board and used as the main controller for face recognition, youth system and locking system. The camera is used to capture images of the person in front of the door. IoT system enables the user to control the door access.


2021 ◽  
Vol 5 (1) ◽  
pp. 19-25
Author(s):  
Nizirwan Anwar ◽  
Budi Tjahjono ◽  
Masmur Tarigan ◽  
Dewanto Adhy Rosian ◽  
Nur Widiyasono ◽  
...  

The main problem in this research is the increase in cases of theft and robbery. This incident was caused by the busyness of everyone in their daily lives, so they forgot about the safety of their house. An Internet of Things (IoT) based home security system that utilizes a PIR sensor as a human motion detector and then sends a notification in the form of a notification via SMS or e-mail is one solution to overcome the problems that have been previously proposed in previous research. However, to further clarify the warning sent from the system, a home security system is needed that can attach an image to the notification. In this study, an IoT-based home security system was developed. The IoT security system being developed can automatically send a warning message by attaching an image when the PIR sensor detects human presence. The IoT system requires a Raspberry Pi as a microcontroller that is connected to the internet, a PIR sensor to detect human movement and a Pi Camera to win an image when there is an encounter with a human within the range of the PIR sensor. Experiments in this study show that the IoT system can automatically send warning messages via the Telegram application by attaching an image when the PIR sensor detects human presence in various light conditions with a distance of 0-5 meters and the speed of sending email alerts is influenced by the condition of the internet network connection and file size. images sent.


2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Eko Riyanto

Smart Home is one of the tools that is developed for ease of automation of smart home management from the start of address, security, comfort, savings, through automation with Android. In designing a micro home security system controller using raspberry pi 3 and Android smartphones that can reduce the number of criminal acts of burglary door. This tool consists of an electro magnetic door lock called a solenoid door lock.This solenoid key is placed on the door of the house for security. The design of this home door security system utilizes Raspberyy pi b + as a control device from near and far by utilizing the wifi network and sms gateway to control opening and closing the home door lock that is controlled via an android mobile. Through web bootstrap that will display the results captured by the camera to provide a home situation every time someone enters.This house door security system that has been successfully built and tested with the working principle if there is someone who forces or breaks the house door in a closed condition, the system will activate a warning or alarm by sounding the buzzer¸ because there is an LDR sensor connected to one switch connected to the solenoid key that results, if the key is opened with a security system then the LDR sensor will turn off and there will be no alarm, but if it is forced to break the LDR sensor will activate and read the movement of the door so that the reaction occurs and the buzzer alarm will sound. This security system is a solution to increase the level of home security ¸ besides this sophisticated system is very easy to use and integrated with android smartphones


2020 ◽  
Vol 8 (3) ◽  
pp. 210-216
Author(s):  
Subiyanto Subiyanto ◽  
Dina Priliyana ◽  
Moh. Eki Riyadani ◽  
Nur Iksan ◽  
Hari Wibawanto

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.


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