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JBE, vol. 23, no. 5, pp.642-655, September, 2018

DOI: https://doi.org/10.5909/JBE.2018.23.5.642

Online Human Tracking Based on Convolutional Neural Network and Self Organizing Map for Occupancy Sensors

Jong In Gil and Manbae Kim

C.A E-mail: on-line tracking, convolutional neural network, self organizing map, occupancy sensor

Abstract:

Occupancy sensors installed in buildings and households turn off the light if the space is vacant. Currently PIR(pyroelectric infra-red) motion sensors have been utilized. Recently, the researches using camera sensors have been carried out in order to overcome the demerit of PIR that cannot detect stationary people. The detection of moving and stationary people is a main functionality of the occupancy sensors. In this paper, we propose an on-line human occupancy tracking method using convolutional neural network (CNN) and self-organizing map. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. Using videos capurted from an overhead camera, experiments have validated that the proposed method effectively tracks human.



Keyword: on-line tracking, convolutional neural network, self organizing map, occupancy sensor

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