用点云谱分析实现高精度隐私保护人体动作识别
LiDAR-based Human Activity Recognition through Laplacian Spectral Analysis
- 将点云转为邻近图，通过拉普拉斯谱提取姿态特征
- 在MM-Fi数据集上13类康复动作达94.4%准确率，全27类90.3%
- 无需深度学习，特征可解释，适合医疗等敏感场景
人体动作识别在医疗、制造和人机交互中有广泛应用。激光雷达点云提供了一种比摄像头更隐私保护且抗光照干扰的替代方案。本文提出一种基于图谱分析的人体动作识别方法：将每帧激光雷达数据映射为邻近图（epsilon-graph），计算拉普拉斯谱，其特征值及特征向量统计量构成姿态描述符，再通过滑动窗口的时间统计生成固定维度向量，用于支持向量机与随机森林分类。在包含40名受试者、27种动作的MM-Fi数据集上，采用严格的受试者独立测试协议，该方法在13类康复动作上达到94.4%准确率，在全部27类动作上达到90.3%准确率，优于已报道的基于骨架的基线方法。贡献在于直接从点云几何结构中提取出紧凑且可解释的特征集，为端到端深度学习提供了高效准确的替代方案。
原文摘要 · Abstract (English)
Human Activity Recognition supports applications in healthcare, manufacturing, and human-machine interaction. LiDAR point clouds offer a privacy-preserving alternative to cameras and are robust to illumination. We propose a HAR method based on graph spectral analysis. Each LiDAR frame is mapped to a proximity graph (epsilon-graph) and the Laplacian spectrum is computed. Eigenvalues and statistics of eigenvectors form pose descriptors, and temporal statistics over sliding windows yield fixed vectors for classification with support vector machines and random forests. On the MM-Fi dataset with 40 subjects and 27 activities, under a strict subject-independent protocol, the method reaches 94.4% accuracy on a 13-class rehabilitation set and 90.3% on all 27 activities. It also surpasses the skeleton-based baselines reported for MM-Fi. The contribution is a compact and interpretable feature set derived directly from point cloud geometry that provides an accurate and efficient alternative to end-to-end deep learning.
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