Chandana A N

Work place: JSS Academy of Technical Education Bengaluru/ Department of Information Science and Engineering, Bengaluru, 560060, India



Research Interests: Machine Learning


Chandana A N pursuing Bachelor of Engineering final year in the Department Information Science and Engineering at JSS Academy of Technical Education, Bengaluru, under the Visveswaraya Technological University. Area of interest are IoT, Machine learning and Cloud.

Author Articles
IOT Based Burglar Detection and Alarming System Using Raspberry Pi

By Sahana V Shashidhar R Bindushree R Chandana A N

DOI:, Pub. Date: 8 Dec. 2023

In today’s world, security has become the most difficult task. With increasing urbanization and the growth of big cities, the crime graph is also on the rise. In order to ensure the security and safety of our home while we are away, we propose the use of Raspberry Pi to implement an IOT-based burglar detection and alert system. IoT involves the improvement of networks to efficiently acquire and inspect statistics from different sensors and actuators, then send the statistics via Wi-Fi connection to a personal smartphone or laptop. The concept of antitheft devices has been around for decades, but most are only CCTVs, IP cameras, or magnetic doorbells. There is a limited amount of work devoted to face recognition and weapon detection. The design of anti-theft protection devices relies primarily on face recognition and remote tracking. Here, our objective is to improve this system by incorporating weapon detection feature by image processing. The system uses Raspberry Pi, in which a person is only permitted access to the house if his/her face is recognized by the proposed system, and if he/she does not carry any weapons. From the standpoint of security, this system is more reliable and efficient. The proposed system is intended to develop a secure access control application based on face recognition along with weapon detection. By using the Telegram app, the proprietor can monitor the digital camera mounted on the door frame. As a means of improving the accuracy and efficiency of our system, we use the Python language and the Open CV library.

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