Human Behavior Recognition Using Range-Velocity-Time Points

Abstract

Radar-based sensors do not require optimal lighting and atmospheric conditions and nonocclusion, making them a promising solution for human behavior analysis in complex environments. Existing radar-based models generally retrieve features from either the time-velocity domain or the time-range domain. Such two-dimensional representations cannot fully depict dynamic human motion features. In this paper, we propose a temporal range-Doppler PointNet-based method to analyze human behavior. We transform human echoes to 3D point sets and then feed them into the hierarchical PointNet model for classification. The proposed point network can learn structural features from the micromotion trajectory more effectively than directly processing the raw point cloud. To further improve our model's robustness in practical applications, we design an outlier detection module for detecting anomalies such as in multitarget scenarios. The results of experiments on motion capture databases and range-Doppler radar measurements demonstrate that our method realizes outstanding performance in terms of the classification accuracy, noise robustness, and anomaly detection accuracy.

Keywords

• Human activity recognition
• micro-Doppler effect
• deep learning
• point set
• range-Doppler processing
• ultra-wideband radar