用轻量模型高效处理毫米波雷达点云，实现高精度人体活动识别。
OG-PCL: Efficient Sparse Point Cloud Processing for Human Activity Recognition
- 设计三视图并行结构，兼顾空间信息与计算效率。
- 参数仅0.83M，在RadHAR数据集上达91.75%准确率。
- 适合部署在资源受限的实时雷达感知设备上。
基于毫米波雷达的人体活动识别（HAR）提供了隐私保护且鲁棒的替代方案，优于摄像头和可穿戴设备。本文提出占用门控并行卷积-双向长短期记忆网络（OG-PCL），用于处理毫米波传感生成的稀疏三维点云。该模型参数量仅为0.83M，可在RadHAR数据集上达到91.75%的准确率，优于2D CNN、PointNet和3D CNN等现有方法。通过消融实验验证了三视图并行结构在保留三维空间信息方面的优势。进一步引入占用门控卷积（OGConv）模块，证明其占用补偿机制对处理稀疏点云至关重要。所提OG-PCL为轻量级平台上的实时雷达式人体活动识别提供了紧凑而精确的解决方案。
原文摘要 · Abstract (English)
Human activity recognition (HAR) with millimeter-wave (mmWave) radar offers a privacy-preserving and robust alternative to camera- and wearable-based approaches. In this work, we propose the Occupancy-Gated Parallel-CNN Bi-LSTM (OG-PCL) network to process sparse 3D radar point clouds produced by mmWave sensing. Designed for lightweight deployment, the parameter size of the proposed OG-PCL is only 0.83M and achieves 91.75 accuracy on the RadHAR dataset, outperforming those existing baselines such as 2D CNN, PointNet, and 3D CNN methods. We validate the advantages of the tri-view parallel structure in preserving spatial information across three dimensions while maintaining efficiency through ablation studies. We further introduce the Occupancy-Gated Convolution (OGConv) block and demonstrate the necessity of its occupancy compensation mechanism for handling sparse point clouds. The proposed OG-PCL thus offers a compact yet accurate framework for real-time radar-based HAR on lightweight platforms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。