MCROOD: Multi-Class Radar Out-Of-Distribution Detection
This addresses safety in deploying deep learning for radar-based human activity recognition, with incremental improvements in speed and performance for real-time applications.
The paper tackles out-of-distribution detection for radar images by proposing a reconstruction-based multi-class detector that identifies moving objects other than specific human activities, achieving AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes.
Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing.