Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals
This paper investigates the processing of Frequency-Modulated Continuous-Wave (FMCW) radar signals for vehicle classification. In the last years, deep learning has gained interest in several scientific fields and signal processing is not one exception. In this work we address the recognition of the vehicle category using a Convolutional Neural Network (CNN) applied to range-Doppler signatures. The developed system first transforms the 1-dimensional signal into a 3-dimensional signal that is subsequently used as input to the CNN. When using the trained model to predict the vehicle category, we obtained good performance.
The authors wish to tank Infomobility S.R.L. Concordia sulla Secchia (Modena, Italy) and Autostrade per l’Italia (Roma, Italy) for having provided the radar data.
- 1.Munoz-Ferreras, J.M., Calvo-Gallego, J., and Perez-Martinez, F. 2008. Monitoring road traffic with a high resolution lfmcw radar. In IEEE Radar Conference, 1–5.Google Scholar
- 3.De Angelis, G., A. De Angelis, V. Pasku, A. Moschitta, and P. Carbone. 2016 A simple magnetic signature vehicles detection and classification system for smart cities. In IEEE International Symposium on Systems Engineering (ISSE 2016), 1–6.Google Scholar
- 4.H. Sandhawalia, J. A. Rodriguez-Serrano, H. Poirier, and G. Csurka. 2013. Vehicle type classification from laser scanner profiles: A benchmark of feature descriptors. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 517–522.Google Scholar
- 5.Sermanet, Pierre, David Eigen, Xiang Zhang, Michaël Mathieu, Robert Fergus, and Yann Lecun. 2014. Overfeat: Integrated recognition, localization and detection using convolutional networks.Google Scholar
- 6.Fang, J., H. Meng, H. Zhang, and X. Wang. 2007. A low-cost vehicle detection and classification system based on unmodulated continuous-wave radar. In 2007 IEEE Intelligent Transportation Systems Conference, 715–720.Google Scholar
- 7.Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In CVPR 2015.Google Scholar
- 8.K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.Google Scholar
- 9.Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. CVPR (to appear).Google Scholar
- 10.Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015), pages 1026–1034.Google Scholar
- 11.J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.Google Scholar
- 12.Yann Lecun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, 2278–2324.Google Scholar
- 13.Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25, ed. F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, 1097–1105. Curran Associates, Inc.Google Scholar
- 15.Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27, ed. Z. Ghahramani, M. Welling, C. Cortes, N.d. Lawrence, and K.q. Weinberger, 3320–3328. Curran Associates, Inc.Google Scholar