Master Practical Training Project Status: available Supervisors: Irene Ballester, Martin Kampel Problem Statement This project aims to address the challenges associated with the expensive and time-consuming annotation of 3D data by exploring a self-supervised approach for the extraction of 4D spatio-temporal features from dynamic point cloud data. Specifically, the project investigates the prediction of the temporal … Continue reading Self-Supervised 4D Point Cloud Feature Learning for Activity Recognition →

Master Practical Training Project/ Master’s thesis Status: available  taken (but if you would like to work on a similar topic, contact Irene Ballester) Supervisors: Irene Ballester, Martin Kampel Problem Statement Human Activity Recognition (HAR) in computer vision, a pivotal area for healthcare, security, and robotics, often relies on privacy-invading RGB cameras. To enhance HAR accuracy while … Continue reading Human Activity Recognition from Real-World Depth Images →