Vibration Guided Automatic Vision for Enhanced Security
Ipsita Sanyal1, K. R. Dhavana2, Kailash T. V.3, Kruthika R.4, Bhavanishankar K.5
1Ms. Ipsita Sanyal, Department of Electronics and Communication Engineering, RNSIT Bangalore (Karnataka), India.
2Ms. K. R. Dhavana, Department of Electronics and Communication Engineering, RNSIT Bangalore (Karnataka), India.
3Mr. Kailash T. V., Department of Computer Science Engineering, RNSIT Bangalore (Karnataka), India.
4Ms. Kruthika R., Department of Electronics and Communication Engineering, RNSIT Bangalore (Karnataka), India.
5Dr. Bhavanishankar K., Assistant Professor, Department of Computer Science, RNSIT Bangalore (Karnataka), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 301-306 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10761292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1076.1292S19
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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: The existing security systems are secure but are not smart enough to handle arbitrary scenarios leading to many false triggers of the alert system. Furthermore, these systems require constant human intervention which isdifficult to achieve.They are also vulnerable as they contain many loopholesand the sensors used are easily manipulatable. The proposed system tries to solve this problem in an efficient and a smart way by the use of sensors, AI and IoT which makes the system robust and resistant againstattacks. The system implements advanced face detection via Single Shot Detection and face recognition via Inception Neural Network for recognition of object in a fast and accurate way. This helps the system act according to the situation, thus preventing any damage to theregion which implements this system. In this work the proposed system is implemented and tested as a Home Security System. The system can also be extended to work in other areas like banks, data hubs, museums etc.The overall accuracy of the system was recorded to be 97.95%.
Keywords: CNN, Inception Neural Networks, Internet of Things, Security Systems, Recognition.
Scope of the Article: Vision and Speech Perception