Improving a Background Model for Tracking and Classification of Objects in LiDAR 3D Point Clouds
This thesis studied methods of improving a background model for a data processing pipeline of LiDAR point clouds. For this, two main approaches were evaluated. The first was to implement and compare three different models for detecting ground in a point cloud. These were based on more classical modeling approaches. The second was to use Deep Learning for semantic segmentation of point clouds and t