Smart Home and Thief Detection using AI

Author:

Dr. D. Srihari 1,Kempili Hemavathi 1,P Sreeveni 1,Siddalayyagari Roopa 1,Valligatla Manasa 1,Yellumgudla Thejaswini 1

Affiliation:

1. Sri Venkateswara College of Engineering & Technology (Autonomous), Chittoor, A.P, India

Abstract

In our world increasingly interconnected, demand for smart home solutions that prioritizing security a convenient ever-growing. This abstract presents an inventive approach to home security an automation through integration of ESP32-CAM, ultrasonic sensor, a Firebase technology. Our system offering real-time theft detecting a remote controlling of home appliances via mobile app. By leveraging AI-drive theft detection a Firebase's cloud-based platform, users can monitor a managing their homes from anywhere, enhancing both security a convenient. This abstract outline component, workflow, features, a potential future enhancement of our Smart Home and Theft Detect system, offering a glimpse into the future of home automation.

Publisher

Naksh Solutions

Reference8 articles.

1. VirSinghender, S. Singh, and P. Gupta, “ScienceDirect Real-Time Anomaly Recognition Through CCTV Using Neural Networks,” Procedia Compute. Sci., vol. 173, no. 2019, pp. 254–263, 2020, do: 10.1016/j.procs.2020.06.030.

2. M.T. Bhatti et al. [1] proposed detection of weapons using Deep Learning. CNN based object detector is used for detecting weapons through real time CCTV R. T. Wang, “Title of Chapter,” in Classic Physiques, edited by R. B. Hamil (Publisher Name, Publisher City, 1999), pp. 212–213.

3. Kushwaha A, Mishra A, Kamble K, Janbhare R (2018) Theft detection using machine learning. Int Conf Innov Adv Technol Eng 8:67–71 B. R. Jackson and T. Pitman, U.S. Patent No. 6,345,224 (8 July 2004)

4. Verma GK, Dhillon A (2017) A handheld gun detection using faster r-CNN deep learning. In: Proceedings of the 7th international conference on computer and communication technology. pp 84–88.

5. Pnevmatikakis Aristodemos, "Recognizing daily functioning activities in smart homes", Wireless Personal Communications, vol. 96.3, pp. 3639-3654, 2017.

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