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http://dx.doi.org/10.5909/JBE.2018.23.5.642

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

Gil, Jong In (Dept. of Computer and Communications Engineering, Kangwon National University)
Kim, Manbae (Dept. of Computer and Communications Engineering, Kangwon National University)
Publication Information
Journal of Broadcast Engineering / v.23, no.5, 2018 , pp. 642-655 More about this Journal
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.
Keywords
on-line tracking; convolutional neural network; self organizing map; occupancy sensor;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 C. Bao, Y. Wu, H. Ling and H. Ji, "Real Time Robust L1 Tracking Using Accelerated Proximal Gradient Approach", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1830-1837, 2012.
2 S. Oron, A. Bar-Hillel, D. Levi and S. Avidan, "Locally Orderless Tracking", International Journal of Computer Vision, Vol. 111, No. 2, pp. 213-228, 2015.   DOI
3 T. Zhang, B. Ghanem, S. Liu and N. Ahuja, "Robust Visual Tracking via Multi-task Sparse Learning", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2042-2049, 2012.
4 W. Zhong, H. Lu and MH. Yang, "Robust Object Tracking via Sparsity-based Collaborative Model", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1838-1845, 2012.
5 J. Gil, and M. Kim, "Real-time People Occupancy Detection by Camera Vision Sensor", Journal of Broadcast Engineering, 22(6), pp. 774-784, 2017.   DOI
6 H. Li, Y. Li, and F. Porikli, "DeepTrack: Learning Discriminative Feature Representations Online for Robust Visual Tracking", IEEE Trans. on Image Processing, Vol. 25, No. 4, pp. 1834-1848, April 2016.   DOI
7 K. Zhang, Q. Liu, and M. Yang, "Robust Visual Tracking via Convolutional Networks Without Training", IEEE Trans. on Image Processing, Vol. 25, No. 4, pp. 1779-1792, April 2016.   DOI
8 X. Zhou, L. Xie, P. Zhang, and Y. Zhang, "An Ensemble of Deep Neural Networks for Object Tracking", IEEE Conf. on Image Processing, pp. 843-847, 2014.
9 T. Kohonen, "Self-organized formation of topologically correct feature maps", Biological cybernetics, 43(1), pp. 59-69, 1982.   DOI
10 X. Jia, H. Lu and M. H. Yang, "Visual Tracking via Adaptive Structural Local Sparse Appearance Model", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1822-1829, 2012.
11 J. F. Henriques, R. Caseiro, P. Martin and J. Batista, "Exploiting the Circulant Structure of Tracking-by-Detection with Kernels", European Conf. on Computer Vision, pp. 702-715, 2012.
12 K. Zhang, L. Zhang and M. H. Yang, "Real-time Compressive Tracking", European Conf. on Computer Vision, pp. 864-877, 2012.
13 L. Sevilla-Lara and E. Learned-Miller, "Distribution Fields for Tracking", IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1910-1917, 2012.
14 D. A. Ross, J. Lim, R. S. Lin and M. H. Yang, "Incremental Learning for Robust Visual Tracking", International Journal of Computer Vision, Vol. 77, Issue 1-3, pp. 125-141, 2008.   DOI
15 P. Liu, S. Nguang, and A. Partridge, "Occupancy inference using pyro-electric infrared sensors through hidden markov model", IEEE Sensors Journal, 16(4), Feb, 2016.
16 I. Amin, A. Taylor, F. Junejo, A. Al-Habaibeh, and R. Parkin, "Automated people-counting by using low-resolution infrared and visual cameras", Measurement, 41, 2008.
17 Y. Benezeth, H. Laurent, B. Emile, and C. Rosenberger, "Towards a sensor for detecting human presence and characterizing activity", Energy and Buildings, 43, 2011.
18 J. Han, and B. Bhanu, "Fusion of color and infrared video for moving human detection", Pattern Recognition, 40, 2007.
19 S. Nakashima, Y. Kltazono, L. Zhang, and S. Serikawa, "Development of privacy-preserving sensor for person detection", Proceedia-Social and Behavioral Sciences, 2(1)n, 2010.
20 F. Wahl, M. Milenkovic, and O. Amft, "A distributed PIR-based approach for estimating people count in office environments", IEEE Conf. on Computational Science and Engineering, 2012.