4 papers
SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs
Stefan Hägele, Fabian Seguel, Driton Salihu +2
Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for th…
RadarCNN: Learning-based Indoor Object Classification from IQ Imaging Radar Data
Stefan Hägele, Fabian Seguel, Driton Salihu +2
Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. T…
ACCOR: Attention-Enhanced Complex-Valued Contrastive Learning for Occluded Object Classification Using mmWave Radar IQ Signals
Stefan Hägele, Adam Misik, Constantin Patsch +1
Millimeter-wave (mmWave) radar provides robust sensing under adverse conditions and can penetrate thin materials for non-visual perception in industrial and robotic settings. Recen…
Technical Report for Egocentric Mistake Detection for the HoloAssist Challenge
Constantin Patsch, Marsil Zakour, Yuankai Wu +1
In this report, we address the task of online mistake detection, which is vital in domains like industrial automation and education, where real-time video analysis allows human ope…