提升毫米波雷达点云处理，实现隐私保护下的人体动作识别
Enhanced Sparse Point Cloud Data Processing for Privacy-aware Human Action Recognition
- 结合聚类、匹配与滤波算法优化稀疏雷达数据
- 在MiliPoint数据集上验证三方法组合可提升识别准确率
- 针对隐私敏感场景，兼顾精度与计算效率
人体动作识别（HAR）在健康监护、健身追踪和智能助老中至关重要。传统视觉系统虽有效，但存在隐私风险。毫米波雷达提供隐私保护替代方案，但其点云数据稀疏且噪声大。现有主要处理方法包括基于密度的聚类（DBSCAN）、匈牙利算法和卡尔曼滤波。本文在MiliPoint数据集上对这三种方法及其所有组合（单个、两两、三者全用）进行详细性能评估，考察识别准确率与计算开销。同时针对各方法提出改进策略以提升精度。结果揭示了各方法的优劣与权衡，为后续基于毫米波的HAR系统设计提供关键指导。
原文摘要 · Abstract (English)
Human Action Recognition (HAR) plays a crucial role in healthcare, fitness tracking, and ambient assisted living technologies. While traditional vision based HAR systems are effective, they pose privacy concerns. mmWave radar sensors offer a privacy preserving alternative but present challenges due to the sparse and noisy nature of their point cloud data. In the literature, three primary data processing methods: Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Hungarian Algorithm, and Kalman Filtering have been widely used to improve the quality and continuity of radar data. However, a comprehensive evaluation of these methods, both individually and in combination, remains lacking. This paper addresses that gap by conducting a detailed performance analysis of the three methods using the MiliPoint dataset. We evaluate each method individually, all possible pairwise combinations, and the combination of all three, assessing both recognition accuracy and computational cost. Furthermore, we propose targeted enhancements to the individual methods aimed at improving accuracy. Our results provide crucial insights into the strengths and trade-offs of each method and their integrations, guiding future work on mmWave based HAR systems
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