用低成本雷达实现高精度人体运动方向识别
Exploration of Low-Cost but Accurate Radar-Based Human Motion Direction Determination
- 结合特征连接模型增强微多普勒图特征
- 采用轻量级混合网络实现快速方向判断
- 适合嵌入式雷达系统与实时应用
本文针对雷达人体运动方向识别问题，提出一种低成本且高精度的解决方案。首先生成雷达微多普勒时间图（DTM），再通过特征连接模型实现特征增强；随后采用轻量级视觉变换器-卷积神经网络混合结构进行运动方向判定。该方法在开源数据集上验证有效，具备快速推理能力，适用于资源受限的实时场景。相关代码已开源：https://github.com/JoeyBGOfficial/Low-Cost-Accurate-Radar-Based-Human-Motion-Direction-Determination。
原文摘要 · Abstract (English)
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.
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