Automatic Label Creation Framework for FMCW Radar Images Using Camera Data

• Main Authors: Javier Mendez, Stephan Schoenfeldt, Xinyi Tang, Jakob Valtl, M. P. Cuellar, Diego P. Morales
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Sensor fusion; machine learning algorithms; deep learning; radar; auto-labeling system
• Online Access: https://ieeexplore.ieee.org/document/9448072/

Data acquisition and treatment are key issues for any Deep Learning (DL) technique, especially in computer vision tasks. A big effort must be done for the creation of labeled datasets, due to the time this task requires and its complexity in cases where different sensors must be used. This is the case of radar imaging applications, where radar data are difficult to analyze and must be labeled manually. In this paper, a semi-automatic framework to generate labels for range Doppler maps (radar images) is proposed. This technique is based on a sensor fusion approach with radar and camera sensors. The proposed scheme operates in two steps: The first step is the environment features extraction, in which the radar data is preprocessed and filtered to remove ghost targets and detect clusters, and camera data are used to extract the information of the targets. In the second step, a rule-based system that considers the extracted features fuses the information to generate labels for the radar data. By using the proposed framework, the experimentation performed suggests that the time required to label the data is reduced as well as the possibility of human error during the labeling task. Our results show that the proposed technique can improve the final model accuracy with regards the traditional labeling method, carried out by human experts.