Enhanced Sparse Point Cloud Data Processing for Privacy-aware Human Action Recognition
This work addresses privacy concerns in human action recognition for applications like healthcare and fitness tracking, but it is incremental as it builds on and optimizes established methods.
The paper tackles the challenge of processing sparse and noisy point cloud data from mmWave radar sensors for privacy-aware human action recognition by evaluating and enhancing three existing methods (DBSCAN, Hungarian Algorithm, Kalman Filtering) individually and in combination, achieving improved recognition accuracy with concrete performance insights on the MiliPoint dataset.
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