RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-31435-3_23
Reference43 articles.
1. Angelov, A., Robertson, A., Murray-Smith, R., Fioranelli, F.: Practical classification of different moving targets using automotive radar and deep neural networks. IET Radar Sonar Navig. 12(10), 1082–1089 (2018). https://doi.org/10.1049/iet-rsn.2018.0103. https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-rsn.2018.0103
2. Bansal, K., Rungta, K., Bharadia, D.: RadSegNet: a reliable approach to radar camera fusion (2022). https://doi.org/10.48550/ARXIV.2208.03849. https://arxiv.org/abs/2208.03849
3. Behley, J., et al.: Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: the SemanticKITTI Dataset. Int. J. Robot. Res. 40(8–9), 959–967 (2021). https://doi.org/10.1177/02783649211006735
4. Cao, P., Xia, W., Ye, M., Zhang, J., Zhou, J.: Radar-ID: human identification based on radar micro-doppler signatures using deep convolutional neural networks. IET Radar Sonar Navig. 12(7), 729–734 (2018). https://doi.org/10.1049/iet-rsn.2017.0511. https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/iet-rsn.2017.0511
5. Advances in Intelligent Systems and Computing;S Capobianco,2018
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