Semantic and Instance Segmentation of Radar Point Clouds using Deep Learning
In the context of surveillance, radars are used for object detection and classification. This thesis considers a four-step pipeline for radar point cloud analysis: (1) measurement, (2) clustering, (3) tracking, and (4) classification. The clustering step of the pipeline uses only the spatial coordinates of the points to group them together. By performing semantic segmentation before the clustering