Abstract
In the domain of human action classification, information obtained by optical equipment is used as the input source in most case. However, these optical devices are often affected by environmental factors, which bring some trouble to human motion recognition. The radar signal can avoid these situations, therefore the radar signal has been more and more significant part in this domain. In recent research, most researchers used the Micro-Doppler spectrogram of radar to classify the action. It only makes use of the time-frequency domain information, but ignores the spatial domain information. However, for the radar signal of the human motion, there should be some differences between the information in the time-frequency domain and the information in the other domain such as the space domain. In this case, we employed a CNN with two streams, which can use both Micro-Doppler spectrogram and the echo of the radar to recognize the action. In our architecture, there are two separate CNN processing streams for extracting the features of two stream of input, and the features are combined with a late network. The experiment proves that our method has better accuracy than the input of a single stream.
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
References
Lohit S., Kulkarni K., Turaga P.: Direct inference on compressive measurements using convolutional neural networks. In: IEEE International Conference on Image Processing. IEEE (2016)
Simonyan K., Zisserman A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
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)
Thavaparan, T., Abrol, S., Riseborough, E., et al.: Analysis of radar micro-doppler signatures from experimental helicopter and human data. IET Radar Sonar Navig. 1(4), 289–299 (2007)
Tahmoush, D., Silvious, J.: Radar micro-doppler for long range front-view gait recognition. In: IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2009)
Padar M.O., Ertan A.E., Candan C.: Classification of human motion using radar micro-doppler signatures with hidden Markov models. In: Radar Conference. IEEE (2016)
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)
He, Y., Yang, Y., Lang, Y., Huang, D., Jin, T., Hou, C.: Human activity classification based on radar micro-doppler image with convolutional neural network. In: ICIP (2017)
Du, H., He, Y., Jin, T.: Transfer learning for human activities classification using micro-doppler spectrograms. In: IEEE International Conference on Computational Electromagnetics (2018)
He, Y., Molchanov, P., Sakamoto, T., Aubry, P., Le Chevalier, F., Yarovoy, A.: Range-doppler surface: a tool to analyse human target in ultra-wideband radar. IET Radar Sonar Navig. 9(9), 1240–1250 (2015)
Nanzer, J.A.: Microwave and Millimeter-Wave Remote Sensing for Security Applications. Artech House, Norwood (2012)
Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M., Burgard, W.: Multimodal deep learning for robust RGB-D object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 681–687, Hamburg (2015)
Carnegie Mellon University Motion Capture Database. http://mocap.cs.cmu.edu
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, H., He, Y., Huang, H., Jing, X. (2020). Two-Stream Convolutional Neural Network for Action Recognition in Radar. In: Wang, Y., Fu, M., Xu, L., Zou, J. (eds) Signal and Information Processing, Networking and Computers. Lecture Notes in Electrical Engineering, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-15-4163-6_3
Download citation
DOI: https://doi.org/10.1007/978-981-15-4163-6_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4162-9
Online ISBN: 978-981-15-4163-6
eBook Packages: EngineeringEngineering (R0)