A Video Surveillance System for Unmanned Surveillance of Cantonment Boundary
Tarun Kumar1, Sanjeev kr. Pippal2, Aishwarya Mishra3, Allora Dudi4, Vinod Chaudhary5
1Tarun Kumar*, CSED, G. L. Bajaj Institute of Technology and Management, India.
2Sanjeev Kumar Pippal, CSED, G. L. Bajaj Institute of Technology and Management, India.
3Aishwarya Mishra, CSED, G. L. Bajaj Institute of Technology and Management, India.
4Allora Dudi, CSED, Engineering College Bikaner, Bikaner India.
5Vinod Chaudhary, CSED, Engineering College Bikaner, Bikaner India.
Manuscript received on May 05, 2020. | Revised Manuscript received on May 19, 2020. | Manuscript published on June 10, 2020. | PP: 347-351 | Volume-9 Issue-8, June 2020. | Retrieval Number: 100.1/ijitee.K14660981119| DOI: 10.35940/ijitee.K1466.069820
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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: Since last few years, the Incidents that breach internal security and attack on the security forces are increasing day by day. These are security issues are becoming challenging to handle manually due to economical restrictions. This paper proposes an application for video surveillance to handle and monitor the intrusive incidents. The proposed application includes then human detection in no men’s land around the boundary of the army cantonment. The human detection approach is proposed in this paper is developed with integration of the object detection using background subtraction, feature extraction using CNN and object classification into human and non human using SVM. The proposed approach achieves 95.6% accuracy in human detection. Application proposed in this paper is useful for unmanned surveillance of cantonment boundary.
Keywords: Object Detection, Classification, CNN, Alex Net, SVM.
Scope of the Article: Classification