Indoor Localisation through Probabilistic Ontologies
AbstractFor elderly people that are living alone in their homes there is a need to permanently monitor them. One of this aspect consist in knowing their indoor position and motion behavioural status, in real time. One possibility for indoor positioning of an user consists in understanding the images provided by supervising cameras. In this case the main aspect is represented by recognition of objects from these images. Thus, object recognition plays an essential part in understanding the environment and adding meaning to it. This paper presents a method for indoor localisation based on identifying the user’s context. The user’s context is computed based on object recognition and using a probabilistic ontology. The key element is represented by the probabilistic ontology that describes objects, scenes and relations between them. This ontology contains probabilistic relations that are learned using a large database. Results show that given a set of object detectors with high detection rate and low false positive rate, the system can recognize the user’s context with high accuracy.
 DiCarlo, J.; Zoccolan, D.; Rust, N. C.(2012). How does the brain solve visual object recognition, Neuron, 73(3), 415–434, 2012.
 Enns, J. T.; (2004). The Thinking Eye, The Seeing Brain: Explorations in Visual Cognition, W. W. Norton Company, ISBN: 0393977218, 2004
 Fischler, M.A.; Elschlager, R.A., (1973). The Representation and Matching of Pictorial Structures, IEEE Transactions on Computer, 22(1), 67–92, 1973.
 Felzenszwalb, P.F, Huttenlocher, D.P.; (2005), Pictorial Structures for Object Recognition, International Journal of Computer Vision, 61(1):55–79, 2005.
 Gupta, S.; Girshick, R.; Arbelaez, P.; Malik, J. (2014). Learning Rich Features from RGB-D Images for Object Detection and Segmentation, ECCV, 345–360, 2014.
 Kaiser, L.; Gomez, A. N.; Shazeer, N.; Vaswani, A.; Parmar, N.; Jones,l.; Uszkoreit,J (2017). One Model To Learn Them All, http://arxiv.org/abs/1706.05137, last accessed October 2018.
 Li, B.; Wu, T.; Shuai1, S.; Zhang, L.; Chu, R., (2017). Object Detection via Aspect Ratio and Context Aware Region-based Convolutional Networks, arXiv:1612.00534v2 , https://arxiv.org.
 Leal-Taixe, L. (2016). Multiple Object Tracking with Context Awareness, http://arxiv.org/abs/1411.7935, last accessed October 2018.
 Maturana, D.; Scherer., S. (2015). Voxnet: A 3d Convolutional Neural Network for Realtime Object Recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems, 922–928, 2015.
 Napoletan, A. (2015). 10 Best (and Worst) Apps for Caregivers, https://www.aplaceformom.com/blog/best-and-worst-apps-for-caregivers-07-03-2013/, last accessed October 2018.
 Perko, R.; Leonardis, A., (2010). Context Awareness for Object Detection, Computer Vision and Image Understanding, 114(6), 700–711, 2010.
 Rehman, Z.; Kifor C.K. (2016). An Ontology to Support Semantic Management of FMEA Knowledge, International Journal of Computers Communications & Control, 11(4), 507-521, 2016.
 Ren, S.; He, K.; Girshick, R.B.; Sun, J. (2015). Faster R-CNN: towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 91–99, 2015.
 Russell, B. C.; Torralba, A.; Murphy, K. P.; Freeman, W. T. (2008). LabelMe: a Database and Web-Based Tool for Image Annotation, International Journal of Computer Vision, 77(1-3), 157–173, 2008.
 Sabour, S.; Frosst, N.; Hinton, G. E. (2017). Dynamic Routing Between Capsules, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA,3859–3869, https://arxiv.org/pdf/1710.09829.pdf, last accessed October 2018.
 Szegedy, C.; Ioffe, S.; Vanhoucke, V. ( 2016). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, http://arxiv.org/abs/1602.07261, last accessed October 2018.
 Viola, P. A.; Jones, M. J. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1-9, 2001.
 YOLO network, https://pjreddie.com/darknet/yolo/, last accessed October 2018.
 https://senion.com/indoor-positioning-system/, last accessed October 2018.
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