Learning-Based Human Detection via Radar for Dynamic and Cluttered Indoor Environments

Radar-based human detection draws significant attention in response to growing safety concerns driven by advances in factory automation and smart home technologies. However, much of this research typically operates in controlled environments characterized by minimal clutter and noise, which limits their effectiveness in real-world scenarios such as urban areas, factories, and smart homes. In this study, we address this limitation by collecting a real-world radar dataset in dynamic and cluttered indoor environments, spanning five distinctive environments. To simulate non-human targets, we introduce a moving trolley. Subsequently, we propose a system including simple Radar Signal Processing steps and a learning-based model using unsupervised domain adaptation to enhance its adaptability to unseen environments. Through a series of comprehensive experiments employing popular learning-based methods on our dataset, we demonstrate the model’s efficacy in mitigating environmental interference and successfully adapting to previously unseen environments.