Development of Specific Area Intrusion Detection System using YOLO in CCTV Video
Geun Tae Kim1, Yeonghun Lee2, Kyounghak Lee3, Hyung Hwa Ko4
1GeunTae Kim, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, Korea.
2Yeonghun Lee, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, Korea.
3Kyounghak Lee, IACF, Kwangwoon Univ., Seoul, Korea.
4Hyung Hwa Ko, Department of Electronics and Communications Engineering, Kwangwoon University, Seoul, Korea.
Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 852-856 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11440688S219/19©BEIESP
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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: Recently, many things are monitored with CCTV. It needs manpower to monitor. Therefore, we propose a system that stores the object’s data when the object enters a specific area. Generally, You Only Look Once (YOLO) method is used for object detection. We propose a system that increases the recognition rate by using new dataset because the recognition rate may not be accurate when using existing VOC weight, and can apply to intrusion detection. The Proposed model with the training data created on the particular conditions precisely detects proper objects and reduces effect by a variance of frame and brightness. The object was found more precisely with newly trained weights instead of using VOC weight. The dataset is made to fit to the specific situation, and it can be used for genera situation. When using the VOC weight, object detection was affected by the frame change and the brightness change of the light, but the effect was reduced by using new weight.
Keywords: CCTV, CNN, Intrusion Detection, Object Classification, Object Detection, YOLO
Scope of the Article: Classification