| Autor: |
Yinan Zhao, Tao Liu, Xiang Feng, Zhanfeng Zhao, Wenqing Cui, Yu Fan |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 14, Iss 20, p 5177 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs14205177 |
| Popis: |
In recent years, non-contact human–computer interactions have aroused much attention. In this paper, we mainly propose a dual view observation system based on the frontal and side millimeter-wave radars (MWR) to collect echo data of the Air writing digits “0~9”, simultaneously. Additionally, we also propose a novel distance approximation method to make the trajectory reconstruction more efficient. To exploit these characteristics of spatial-temporal adjacency in handwriting digits, we propose a novel clustering algorithm, named the constrained density-based spatial clustering of application with noise (CDBSCAN), to remove background noise or clutter. Moreover, we also design a robust gesture segmentation method by using twice-difference and high–low thresholds. In our trials and comparisons, based on the trajectories formulated by echo data series of time–distance and time–velocity of dual views, we present a lightweight-based convolution neural network (CNN) to realize these digits recognition. Experiment results show that our system has a relatively high recognition accuracy, which would provide a feasible application for future human–computer interaction scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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