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http://dx.doi.org/10.5909/JBE.2018.23.2.274

CNN-based People Recognition for Vision Occupancy Sensors  

Lee, Seung Soo (Department of Computer and Communications Eng., Kangwon National University)
Choi, Changyeol (Department of Computer and Communications Eng., Kangwon National University)
Kim, Manbae (Department of Computer and Communications Eng., Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.23, no.2, 2018 , pp. 274-282 More about this Journal
Abstract
Most occupancy sensors installed in buildings, households and so forth are pyroelectric infra-red (PIR) sensors. One of disadvantages is that PIR sensor can not detect the stationary person due to its functionality of detecting the variation of thermal temperature. In order to overcome this problem, the utilization of camera vision sensors has gained interests, where object tracking is used for detecting the stationary persons. However, the object tracking has an inherent problem such as tracking drift. Therefore, the recognition of humans in static trackers is an important task. In this paper, we propose a CNN-based human recognition to determine whether a static tracker contains humans. Experimental results validated that human and non-humans are classified with accuracy of about 88% and that the proposed method can be incorporated into practical vision occupancy sensors.
Keywords
occupancy sensor; camera; CNN; people recognition; tracking;
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Times Cited By KSCI : 1  (Citation Analysis)
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