DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds

DING Xuanyu; JIN Biao; ZHANG Zhenkai

doi:10.11999/JEIT251087

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DING Xuanyu, JIN Biao, ZHANG Zhenkai. DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251087

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DING Xuanyu, JIN Biao, ZHANG Zhenkai. DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251087

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DGCN-MFW: A Lightweight Human Action Recognition Network for Millimeter-Wave Radar 3D Point Clouds

doi: 10.11999/JEIT251087 cstr: 32379.14.JEIT251087

Jiangsu University of Science and Technology, Ocean College, Zhenjiang 212003, China

Funds: This work was supported by the National Natural Science Foundation of China (62571220), Key Research and Development Project of Henan Province (241111212500), Science and Technology Plan (Basic Research) Project of Zhenjiang City (Grant No. JC2025026)

Accepted Date: 2026-03-03
Rev Recd Date: 2026-03-03

Available Online: 2026-03-15

Abstract

Abstract

Objective Millimeter-wave radar 3D point clouds offer key spatial cues for human action recognition, but their inherent disorder challenges feature extraction, and actions depend on multi-frame temporal correlations, making single-frame analysis error-prone. This paper propose a dynamic graph convolutional network that fuses multi-scale features, adaptively weights frames, and uses cross-attention, tailored to long 3D point-cloud sequences to improve recognition performance and efficiency. Methods This paper proposes a dynamic graph convolutional network solution (DGCN-MFW) with three core components: dynamic graph convolution feature extraction, multi-scale feature fusion, and adaptive temporal frame weighting. Step 1: Use dynamic graph convolution to automatically build spatial geometry via local directed neighborhood graphs and update neighborhoods online, avoiding manual graph construction and improving feature robustness. Step 2: Apply multi-scale feature fusion to jointly extract and integrate point-cloud features across space and time, capturing local details and global semantics. Step 3: Introduce adaptive frame weighting to learn per-frame importance, highlight discriminative key frames, and suppress noisy or unimportant frames; cross-attention enables information exchange between the center frame and context, compensating for single-frame deficits caused by motion blur, occlusion, or pose ambiguity. Results and Discussions The proposed network model extracts features via dynamic graph convolution, then conducts multi-scale feature fusion and adaptive frame weighting, and ultimately accomplishes human action recognition. It performs excellently on public TI and Vayyar millimeter-wave radar point cloud datasets, with only 2.06M parameters and 4.51 GFLOPS of computation, outperforming existing methods (Tables 2, 3, 4). Ablation experiments prove both core modules significantly boost recognition accuracy (Table 1). Confusion matrices show it hits over 99% accuracy for most actions on the two datasets, exhibiting superior performance (Fig. 10, 11). Nevertheless, its scale, parameter count and efficiency in large-scale data processing need improvement, and future work will focus on model lightweighting and architectural optimization to enhance efficiency. Conclusions To address the two major challenges in mmWave radar 3D point-cloud human action recognition, this paper proposes an action recognition algorithm based on dynamic graph convolutional network and multi-feature fusion. It uses a multi-scale feature fusion module and cross-scale interaction to extract local and global features, improving spatial representation. An adaptive frame-weighting module and cross-attention mechanism are adopted to capture the temporal evolution of actions. The method achieves 98.32% and 99.48% accuracies on two datasets with 2.06M parameters and 4.51 GFLOPs, outperforming mainstream models. It provides a new solution for high-precision and low-resource mmWave radar action recognition, suitable for real-time scenarios like industrial human–machine interaction, intelligent security and healthcare.
- Millimeter-wave radar 3D point cloud,
- Human action recognition,
- Graph convolutional network,
- Multi-scale feature fusion,
- Adaptive frame weighting

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References(26)

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