启发视觉的雷达图像质量评估用于基于雷达的人类活动表示
Vision-Inspired Image Quality Assessment for Radar-Based Human Activity Representations
AI总结：
本文提出基于雷达的图像质量评估方法，通过改进去噪和预处理技术，提升动态和静态活动识别的准确性和可解释性。
摘要：
基于雷达的人类活动识别作为一种隐私保护的替代方案，已引起关注，尤其是在长期护理机构等敏感环境中。从FMCW雷达信号中提取的微多普勒光谱是识别动态活动的核心，但其有效性受到噪声和杂波的限制。在本工作中，我们使用基准雷达数据集重新实现并评估了三种最近的去噪和预处理技术：自适应预处理、自适应阈值和基于熵的去噪。为了说明传统度量在低信噪比环境中的不足，我们使用感知图像质量度量和标准误差度量来评估性能。我们还提出了一种新的框架，利用距离-角度特征图进行静态活动识别，以扩展HAR超出动态活动。我们提出了两个重要贡献：一个时间跟踪算法以强制一致性，以及一个无参考质量评分算法以评估RA-图的保真度。根据实验结果，我们提出的方法增强了动态和静态活动的分类性能和可解释性，为更可靠的雷达HAR系统打开了大门。
英文摘要：
Radar-based human activity recognition has gained attention as a privacy-preserving alternative to vision and wearable sensors, especially in sensitive environments like long-term care facilities. Micro-Doppler spectrograms derived from FMCW radar signals are central to recognizing dynamic activities, but their effectiveness is limited by noise and clutter. In this work, we use a benchmark radar dataset to reimplement and assess three recent denoising and preprocessing techniques: adaptive preprocessing, adaptive thresholding, and entropy-based denoising. To illustrate the shortcomings of conventional metrics in low-SNR regimes, we evaluate performance using both perceptual image quality measures and standard error-based metrics. We additionally propose a novel framework for static activity recognition using range-angle feature maps to expand HAR beyond dynamic activities. We present two important contributions: a temporal tracking algorithm to enforce consistency and a no-reference quality scoring algorithm to assess RA-map fidelity. According to experimental findings, our suggested techniques enhance classification performance and interpretability for both dynamic and static activities, opening the door for more reliable radar-based HAR systems.
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