Abstract
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: this https URL.
Abstract (translated)
这项工作是在与我的年轻同事讨论后一时兴起完成的。运动方向角影响微多普勒频谱宽度，因此确定人的运动方向可以为步态识别等下游任务提供重要的先验信息。然而，基于多普勒-时间图（DTM）的方法在同时实现特征增强和运动判断方面仍有改进空间。为此，本文探索了一种低成本但准确的雷达基人类运动方向判定（HMDD）方法。具体来说，首先生成基于雷达的人类步态DTM，然后使用特征链接模型实现特征增强。随后，通过轻量级且快速的视觉变换器-卷积神经网络混合模型结构实施HMDD。本文所提出的方法的有效性已在开源数据集上得到验证。本工作的开源代码发布在：此 https URL 链接。
URL
https://arxiv.org/abs/2507.22567
