Activity Recognition Through Micro-Doppler Image Based on Improved Faster R-CNN
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-7213-2_28
Reference18 articles.
1. Yu, X., Chen, X., Huang, Y., Guan, J.: Fast detection method for low-observable maneuvering target via robust sparse fractional Fourier transform. IEEE Geosci. Remote. Sens. Lett. 17(6), 978–982 (2019)
2. Mou, X., Chen, X., Guan, J., Chen, B., Dong, Y.: Marine target detection based on improved faster R-CNN for navigation radar PPI images. In: 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, pp. 1–5. IEEE (2019)
3. Chen, X., Yu, X., Huang, Y., Guan, J.: Adaptive clutter suppression and detection algorithm for radar maneuvering target with high-order motions via sparse fractional ambiguity function. Appl. Earth Obs. Remote Sens. 13, 1515–1526 (2020)
4. Zhao, R., Ma, X., Liu, X., Li, F.: Continuous human motion recognition using Micro-Doppler signatures in the scenario with micro motion interference. IEEE Sens. J. 21(4), 5022–5034 (2021)
5. Lang, Y., Wang, Q., Yang, Y., Hou, C., Huang, D., Xiang, W.: Unsupervised domain adaptation for Micro-Doppler human motion classification via feature fusion. IEEE Geosci. Remote. Sens. Lett. 16(3), 392–396 (2018)
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