Abstract
Deep convolution neural networks (DCNN) have been an excellent classifier in various fields such as radar images classification. However, the number of training samples deeply influences the performance of networks and radar samples are not easy to be obtained. In this paper, we utilize micro-Doppler simulated data from Kinect sensor and online dataset to train networks and figure out the differences. In order to enhance the reliability of Kinect simulation, a filter for Kinect data is proposed. Later, transfer learning is used to improve the performance. Moreover, bistatic deep convolutional neural networks (bistatic-DCNN) are proposed. After transfer learning, experimental results show that the performances of both 7-class and 13-class motion classification reach over 98%.
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Acknowledgements
This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011517, in part by the Science and Technology Project of Shenzhen under Grant JCYJ20190808142803565, and in part by the National Natural Science Foundation of China under Grant 62101207.
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Lai, J., Chu, P., Yang, Z. (2023). Human Activity Classification Based-On Micro-Doppler Simulations with DNNs and Transfer Learning. In: Dong, J., Zhang, L. (eds) Proceedings of the International Conference on Internet of Things, Communication and Intelligent Technology . IoTCIT 2022. Lecture Notes in Electrical Engineering, vol 1015. Springer, Singapore. https://doi.org/10.1007/978-981-99-0416-7_50
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DOI: https://doi.org/10.1007/978-981-99-0416-7_50
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