• paper link

• Abstract：For fine-grained human perception tasks such as pose estimation and activity recognition, radar sensors show advantages over optical sensors in low-visibility, privacy-aware, and wall-occlusive environments. Most radar-based human perception methods extract information with convolutional neural networks from radar images. This paper proposes RadarFormer, which introduces the self-attention mechanism to perform human perception directly from radar echoes. It bypasses the radar imaging algorithm and realizes end-to-end signal processing. Specifically, we give constructive proof that processing radar echoes using the self-attention mechanism is at least as expressive as processing radar images using the convolutional layer. On this foundation, we design RadarFormer, which is a Transformer-like model to extract human representations from radar echoes and handle various downstream perception tasks. We evaluate the proposed method on human pose estimation, activity recognition, and re-identification tasks. Experimental results demonstrate that our method outperforms state-of-the-art radar-based methods both in performance and computational cost, and obtains accurate human perception results even in dark and occlusive environments.

git clone https://github.com/0809zheng/RadarFormer.git
cd RadarFormer
pip install -r requirements.txt

python train.py

python test.py

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