Exploration of Low-Cost but Accurate Radar-Based Human Motion Direction Determination
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arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916878869331968 |
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| author | Gao, Weicheng |
| author_facet | Gao, Weicheng |
| contents | This work is completed on a whim after discussions with my junior colleague. The motion direction angle affects the micro-Doppler spectrum width, thus determining the human motion direction can provide important prior information for downstream tasks such as gait recognition. However, Doppler-Time map (DTM)-based methods still have room for improvement in achieving feature augmentation and motion determination simultaneously. In response, a low-cost but accurate radar-based human motion direction determination (HMDD) method is explored in this paper. In detail, the radar-based human gait DTMs are first generated, and then the feature augmentation is achieved using feature linking model. Subsequently, the HMDD is implemented through a lightweight and fast Vision Transformer-Convolutional Neural Network hybrid model structure. The effectiveness of the proposed method is verified through open-source dataset. The open-source code of this work is released at: https://github.com/JoeyBGOfficial/Low-Cost-Accurate-Radar-Based-Human-Motion-Direction-Determination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploration of Low-Cost but Accurate Radar-Based Human Motion Direction Determination Gao, Weicheng Signal Processing Computer Vision and Pattern Recognition 68T45 I.5.4 This work is completed on a whim after discussions with my junior colleague. The motion direction angle affects the micro-Doppler spectrum width, thus determining the human motion direction can provide important prior information for downstream tasks such as gait recognition. However, Doppler-Time map (DTM)-based methods still have room for improvement in achieving feature augmentation and motion determination simultaneously. In response, a low-cost but accurate radar-based human motion direction determination (HMDD) method is explored in this paper. In detail, the radar-based human gait DTMs are first generated, and then the feature augmentation is achieved using feature linking model. Subsequently, the HMDD is implemented through a lightweight and fast Vision Transformer-Convolutional Neural Network hybrid model structure. The effectiveness of the proposed method is verified through open-source dataset. The open-source code of this work is released at: https://github.com/JoeyBGOfficial/Low-Cost-Accurate-Radar-Based-Human-Motion-Direction-Determination. |
| title | Exploration of Low-Cost but Accurate Radar-Based Human Motion Direction Determination |
| topic | Signal Processing Computer Vision and Pattern Recognition 68T45 I.5.4 |
| url | https://arxiv.org/abs/2507.22567 |