We offer theses and projects with a focus on machine learning and robotics.
Here, you can explore the current topics:
→ Open Topics
Below are some selected examples of completed works.
We offer theses and projects with a focus on machine learning and robotics.
Here, you can explore the current topics:
→ Open Topics
Below are some selected examples of completed works.
This work investigates privacy-preserving image anonymization for Ambient Assisted Living and Autonomous Driving. It focuses on realistic anonymization, which protects privacy while preserving essential image content, unlike blurring or pixelation. Using DeepPrivacy2, a fully anonymized COCO dataset is created to train and evaluate YOLOv10, analyzing effects on model performance with metrics like mAP and SSIM. The study examines how anonymization alters image characteristics, how object sizes and class co-occurrences influence detection results, and ways to improve future research. Results show challenges such as object obfuscation and re-labeling, emphasizing the need for models and datasets adapted to anonymized data.
This work addresses limitations in current methods for evaluating 6D pose estimators, which often produce low errors even for inaccurate poses, depend heavily on object-specific point clouds, and ignore false detections. Accurate evaluation is essential for robotics, augmented reality, and object manipulation, especially when analyzing performance under disturbances. The thesis proposes a novel error metric and evaluation score that work independently of object geometry and account for false detections. The score is adjustable for different evaluation scenarios. A theoretical discussion and a use case demonstrate its advantages over existing state-of-the-art metrics.