Gabsteiger, JasminJasminGabsteigerMaiwald, TimoTimoMaiwaldWünsche, SimonSimonWünscheWeigel, RobertRobertWeigelLurz, FabianFabianLurz2023-07-272023-07-272023-04IEEE Wireless and Microwave Technology Conference (WAMICON 2023)9798350398649https://hdl.handle.net/11420/42377This paper presents the combination of an existing computer vision model with a highly integrated mm-wave radar system for automated radar data labeling. A pre trained model for person presence detection is used in combination with a camera sensor to determine, whether a person is present or not. The binary prediction is used to label simultaneously measured radar data of the same scenery. The paper addresses the challenges of varying illumination conditions, camera and radar sensor aperture angles and data quality which are dominant influences at the labeling process. The system significantly decreases human effort by automating the labeling process. It is used to generate 10,000 data points, label them and train a neural network with 95.7% accuracy. Finally, a proof-of-concept, training, and evaluation of radar-based activity detection with automatically labeled data is presented. The proposed method can contribute in person presence detection under challenging light conditions.encomputer visionradar data labelingMLE@TUHHConference Paper10.15480/882.802410.1109/WAMICON57636.2023.1012488610.15480/882.8024Conference Paper