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Theft Detection System is a house safety technology that helps in alerting the owner of the house in case of robbery. With our busy lives, it is not possible to monitor it 24*7. Basically, this system is similar to a smart camera which will be able to detect any suspicious weapons or suspicious people entering the house. This system is capable of detecting people with inappropriate gestures or visiting at an unusual time and alerts the user via mail. This system is developed using the Image processing technique in which a dataset of weapons images is created first. Then, a model is built on the dataset using CNN which will be able to detect any weapons used during the robbery. This model is fed to the camera which will be able to alert the owner via mail. This system is further improved to also detect the people with suspicious face gestures like wearing masks that cover parts of the face. Based on the weapon detection and gestures of the guests visiting the guest, a score will be calculated. If the score is very high, an alarm sound is played which alerts the neighbors.
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Roberto Olmos, Siham Tabik, Francisco Herrera, Automatic handgun detection alarm in videos using deep learning, Neurocomputing, Volume 275, 2018, Pages 66-72, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2017.05.012.
F. Pérez-Hernández, S. Tabik, A. Lamas et al., Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance, Knowledge-Based Systems (2020) 105590, https://doi.org/10.1016/j.knosys.2020.105590.
Dinalankara, Lahiru. "Face detection & face recognition using open computer vision classifies." ResearchGate (2017).
Nagrath, Preeti, et al. "SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2." Sustainable cities and society 66 (2021): 102692.
Suresh K1 , Palangappa MB2 , Bhuvan S, “Face Mask Detection by using Optimistic Convolutional Neural Network.” Proceedings of the Sixth International Conference on Inventive Computation Technologies [ICICT 2021] IEEE Xplore Part Number: CFP21F70-ART; ISBN: 978-1-7281-8501-9
Rhowel Dellosa, “Development of an Anti-Theft Device using Motion Detection and Body Temperature”, Asia Pacific Journal of Multidisciplinary Research, December 2014. P-ISSN 2350-7756 | E-ISSN 2350-8442
Xinyi Zhou, Wei Gong, WenLong Fu, Fengtong Du, “Application of Deep Learning in Object Detection”, International Journal of Innovative Research in Advanced Engineering (IJIRAE), June 2014. ISSN: 2349-2163
Yong-Deuk Shin, Jae-Han Park, Ga-Ram Jang, Moon Hong Baeg, “Moving Objects Detection using Freely Moving Depth
Sensing Camera”, 21st International Conference on Pattern Recognition, November 2012.
Ashwini Patil, Shobha Mondhe, Tejashri Ahire, Gayatri Sonar, “Auto-Theft Detection using Raspberry Pi and Android App”, International Journal of Research in Engineering Application & Management (IJREAM), October 2016. ISSN: 2494-9150 10. 10)https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53