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| Citation: | DU Lan, CHEN Xiaoyang, SHI Yu, et al. MMRGait-1.0: A radar time-frequency spectrogram dataset for gait recognition under multi-view and multi-wearing conditions[J]. Journal of Radars, 2023, 12(4): 892–905. doi: 10.12000/JR22227
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MMRGait-1.0: A Radar Time-frequency Spectrogram Dataset for Gait Recognition under Multi-view and Multi-wearing Conditions
doi: 10.12000/JR22227
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Abstract
As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset. -
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References
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- Figure 1. Antenna array distribution of millimeter-wave radar
- Figure 2. Data collection platform and indoor collection scene
- Figure 3. The view of walking
- Figure 4. Examples of three wearing conditions
- Figure 5. Signal processing flow
- Figure 6. Time-frequency spectrograms for eight walking views and three wearing conditions
- Figure 7. Structure of the dataset
- Figure 8. Flow chart of radar gait recognition based on retrieval task
- Figure 9. Framework for feature extraction network model based on retrieval task
- Figure 10. Spatial attention map calculation process
- Figure 11. Long-short time feature extraction module calculation process
- Figure 12. Two sets of time-spectrogram data of walking in different starting states
- Figure 1. Release webpage of MMRGait-1.0: A radar time-frequency spectrogram dataset for gait recognition under multi-view and multi-wearing conditions dataset