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Convolutional neural networks for parking space detection in downfire urban radar

  • Javier Martinez (a1), Dominik Zoeke (a2) and Martin Vossiek (a1)

Abstract

We present a method for detecting parking spaces in radar images based on convolutional neural networks (CNN). A multiple-input multiple-output radar is used to render a slant-range image of the parking scenario and a background estimation technique is applied to reduce the impact of dynamic interference from the surroundings by separating the static background from moving objects in the scene. A CNN architecture, that also incorporates mechanisms to generalize the model to new scenarios, is proposed to determine the occupancy of the parking spaces in the static radar images. The experimental results show very high accuracy even in scenarios where little or no training data is available, proving the viability of the proposed approach for its implementation at large scale with reduced deployment efforts.

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Copyright

Corresponding author

Author for correspondence: Javier Martinez, E-mail: javier.martinez@ieee.org

References

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1United Nations, Department of Economic and Social Affairs, Population Division (2015) World population prospects: The 2015 revision, key findings and advance tables, Working Paper No. ESA/P/WP.241.
2Deng, W, Luo, X, Jiang, L and Luo, Y (2011) Research on video-based monitoring algorithm of parking spaces, in 2011 Third International Conference on Multimedia Information Networking and Security, Nov 2011, pp. 261264.
3Wolff, J, Heuer, T, Gao, H, Weinmann, M, Voit, S and Hartmann, U (2006) Parking monitor system based on magnetic field sensors, in IEEE Intelligent Transportation Systems Conference, pp. 12751279.
4LeCun, Y, Boser, B, Denker, J, Henderson, D, Howard, R, Hubbard, W and Jackel, L (1990) Handwritten digit recognition with a back-propagation network, in Proceedings of Advances in neural information processing systems, pp. 396404.
5Krizhevsky, A, Sutskever, I and Hinton, GE (2012) ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 25, pp. 10971105.
6Kim, Y (2014) Convolutional neural networks for sentence classification, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP 2014).
7Wallach, I, Dzamba, M and Heifets, A (2015) Atomnet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery, arXiv:1510.02855, pp. 1–11.
8Zhang, M, Diao, M and Guo, L (2017) Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access 5, 1107411082.
9Lopez-Risueno, G, Grajal, J and Diaz-Oliver, R (2003) Target detection in sea clutter using convolutional neural networks, in Proceedings of the 2003 IEEE Radar Conference, May 2003, pp. 321328.
10Chen, S, Wang, H, Xu, F and Jin, YQ (2016) Target classification using the deep convolutional networks for SAR images. IEEE Transactions on Geoscience and Remote Sensing 54(8), 48064817.
11Zhou, Y, Wang, H, Xu, F and Jin, YQ (2016) Polarimetric SAR image classification using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters 13(12), 19351939.
12Kim, Y and Moon, T (2016) Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters 13(1), 812.
13Kim, Y and Toomajian, B (2016) Hand gesture recognition using micro-Doppler signatures with convolutional neural network. IEEE Access 4, 71257130.
14Kim, BK, Kang, HS and Park, SO (2017) Drone classification using convolutional neural networks with merged Doppler images. IEEE Geoscience and Remote Sensing Letters 14(1), 3842.
15Zoeke, D and Ziroff, A (2015) Phase migration effects in moving target localization using switched MIMO arrays, in 2015 European Radar Conference (EuRAD), Sept 2015, pp. 8588.
16Stauffer, C and Grimson, WEL (1999) Adaptive background mixture models for real-time tracking, in Proceedings of CVPR 1999, vol. 2, 252.
17Piccardi, M (2004) Background subtraction techniques: a review, in IEEE International Conference on Systems, Man and Cybernetics, vol. 4, Oct 2004, pp. 30993104.
18LeCun, Y, Bengio, Y and Hinton, G (2015) Deep learning. Nature 521(7553), 436444.
19Vedaldi, A and Lenc, K (2015) MatConvNet – convolutional neural networks for MATLAB, in Proceeding of the ACM International Conference on Multimedia 2015.

Keywords

Convolutional neural networks for parking space detection in downfire urban radar

  • Javier Martinez (a1), Dominik Zoeke (a2) and Martin Vossiek (a1)

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