Abstract
Video cameras are arguably the world’s most used sensors for surveillance systems. They give a highly detailed representation of a situation that is easily interpreted by both humans and computers. However, these representations can lose part of their representational value when being recorded in less than ideal circumstances. Bad weather conditions, low-light illumination or concealing objects can make the representation more opaque. A radar sensor is a potential solution for these situations, since it is unaffected by the light intensity and can sense through most concealing objects. In this paper, we investigate the performance of a structured inference network on data of a low-power radar device. A structured inference network applies automated feature extraction by creating a latent space out of which the observations can be reconstructed. A classification model can then be trained on this latent space. This methodology allows us to perform experiments for both person identification and action recognition, resulting in competitive error rates ranging from 0% to 6.5% for actions recognition and 10% to 12% for person identification. Furthermore, the possibility of a radar sensor being used as a complement to a camera sensor is investigated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Inras gmbh (2017). http://www.inras.at
Archer, E., Memming Park, I., Buesing, L., Cunningham, J., Paninski, L.: Black box variational inference for state space models. ArXiv e-prints, November 2015
Chen, V.C., Li, F., Ho, S.S., Wechsler, H.: Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans. Aerosp. Electr. Syst. 42(1), 2–21 (2006). https://doi.org/10.1109/TAES.2006.1603402
Chen, V., Tahmoush, D., Miceli, W.: Radar micro-doppler signatures: processing and applications (2014)
Fioranelli, F., Ritchie, M., Griffiths, H.: Classification of unarmed/armed personnel using the netrad multistatic radar for micro-doppler and singular value decomposition features. IEEE Geosci. Remote Sens. Lett. 12(9), 1933–1937 (2015). https://doi.org/10.1109/LGRS.2015.2439393
Garreau, G., et al.: Gait-based person and gender recognition using micro-doppler signatures. In: 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 444–447, November 2011. https://doi.org/10.1109/BioCAS.2011.6107823
Gurbuz, S.Z., Clemente, C., Balleri, A., Soraghan, J.J.: Micro-doppler-based in-home aided and unaided walking recognition with multiple radar and sonar systems. IET Radar Sonar Navig. 11(1), 107–115 (2017). https://doi.org/10.1049/iet-rsn.2016.0055
Johnson, M., Duvenaud, D.K., Wiltschko, A., Adams, R.P., Datta, S.R.: Composing graphical models with neural networks for structured representations and fast inference. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 2946–2954. Curran Associates, Inc. (2016). http://papers.nips.cc/paper/6379-composing-graphical-models-with-neural-networks-for-structured-representations-and-fast-inference.pdf
Kalgaonkar, K., Raj, B.: Acoustic doppler sonar for gait recogination. In: 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 27–32, September 2007. https://doi.org/10.1109/AVSS.2007.4425281
Kim, Y., Ling, H.: Human activity classification based on micro-doppler signatures using a support vector machine. IEEE Trans. Geosci. Remote Sens. 47(5), 1328–1337 (2009). https://doi.org/10.1109/TGRS.2009.2012849
Kim, Y., Moon, T.: Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 13(1), 8–12 (2016). https://doi.org/10.1109/LGRS.2015.2491329
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Knudde, N., et al.: Indoor tracking of multiple persons with a 77 GHz MIMO FMCW radar. In: 2017 European Radar Conference (EURAD), pp. 61–64, October 2017. https://doi.org/10.23919/EURAD.2017.8249147
Krishnan, R., Shalit, U., Sontag, D.: Structured inference networks for nonlinear state space models (2017). https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14215
Liu, L., Popescu, M., Skubic, M., Rantz, M., Yardibi, T., Cuddihy, P.: Automatic fall detection based on doppler radar motion signature. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 222–225, May 2011. https://doi.org/10.4108/icst.pervasivehealth.2011.245993
Park, J., Javier, R.J., Moon, T., Kim, Y.: Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks. Sensors. 16(12), 1990 (2016)
Tahmoush, D., Silvious, J.: Radar micro-doppler for long range front-view gait recognition. In: 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6, September 2009. https://doi.org/10.1109/BTAS.2009.5339049
Toyer, S., Cherian, A., Han, T., Gould, S.: Human pose forecasting via deep markov models. arXiv preprint arXiv:1707.09240 (2017)
Vandersmissen, B., et al.: Indoor person identification using a low-power FMCW radar. IEEE Trans. Geosci. Remote Sens. PP, 1–12 (2018). https://doi.org/10.1109/TGRS.2018.2816812
Zhang, Z., Andreou, A.G.: Human identification experiments using acoustic micro-doppler signatures. In: 2008 Argentine School of Micro-Nanoelectronics, Technology and Applications, pp. 81–86, September 2008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Polfliet, V., Knudde, N., Vandersmissen, B., Couckuyt, I., Dhaene, T. (2018). Structured Inference Networks Using High-Dimensional Sensors for Surveillance Purposes. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-98204-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98203-8
Online ISBN: 978-3-319-98204-5
eBook Packages: Computer ScienceComputer Science (R0)