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.
Highlights
The recognition of human activities plays an important role in people’s daily life, for instance, in medical, security, and law enforcement applications
We present a temporal range-Doppler PointNet-based approach to track and recognize human behaviors
PointNet-based methods, namely, HP-Net, HP-Net+OSVM, and HP-Net+Scale
Summary
The recognition of human activities plays an important role in people’s daily life, for instance, in medical, security, and law enforcement applications. Since deep learning techniques enable human behavior recognition systems to automatically extract and learn hierarchical features, many of these systems have been designed and achieved promising results. These end-to-end frameworks have been successfully applied in home behavior classification [1], gait analysis [2], and violence detection [3]. The results of experiments on motion capture databases and range-Doppler radar measurements demonstrate that our model achieves significant improvement compared to all the baseline methods that extract information in either image form or raw point clouds This network can be extended to other tasks using range-Doppler radar systems
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