IoT Based Human Intrusion Detection System using Lab View
Kalathiripi Rambabu1, V. Haritha2, S. Nikhil Srinivas3, P. Sanjana Reddy4
1Kalathiripi Rambabu, B.V. Raju Institute of Technology, Narsapur, Medak, Telangana. India.
2V. Haritha, B.V. Raju Institute of Technology, Narsapur, Medak, Telangana. India.
3S. Nikhil Srinivas, B.V. Raju Institute of Technology, Narsapur, Medak, Telangana. India.
4P. Sanjana Reddy, B. V. Raju Institute of Technology, Narsapur, Medak, Telangana. India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 557-560 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F11150486S419/19©BEIESP | DOI: 10.35940/ijitee.F1115.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent times there is an increase in thefts in the recent past. This creates a very bad environment for people to live in fear. The problem with home security in the modern world is a cause for concern. The conventional intruder detection system now we are using are highly expensive and there can be a possibility of false alarms. This problem is fixed by building a home intruder detection system that can accurately detect a human intruder, while filtering out movements that are caused due to any other moving objects using LabVIEW and Python. The images that were acquired and analyzed through frame comparisons are converted to gray scale images and then processed to detect an intruder. Here LabVIEW works as server and Python works as a client. At client video is acquired continuously, video is converted into images. Images are processed and information is send to the server. Server displays the status of the intruder with date and time. If the intruder is present then the system compares the intruder’s data with the data in the system. The images that were acquired and analyzed through frame comparisons converted to gray scale images that represent change, and then filtered through a series of image refining VI’s, helping to enhance our change detection results. If the data matches then the processing stops, if not then the system alerts the user through SMS or email if any intruder has been detected and sends the image to the user app. Through app we can make an alarm.
Keywords: LabVIEW, IOT, Raspberry pi.
Scope of the Article: System Integration