Master Practical Training Project/ Master’s thesis
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 safeguarding privacy, this project employs deep neural networks (GNN, CNN, transformers) with point clouds or skeleton data extracted from real-world depth videos, targeting robust HAR in settings like long-term care facilities.
Advance HAR using depth videos for robust activity recognition in complex environments like care facilities while respecting privacy.
- Implement State-of-the-Art Skeleton Extraction Models from Depth Data: Research and implement leading-edge algorithms for precise skeleton extraction from depth data.
- Preprocess and Annotate Real-World Data: Prepare depth images from care facilities, and annotate activities to enhance dataset quality.
- Adapt Algorithms for Real-World Data: Modify models to handle noisy, diverse real-world data, including data augmentation and robust feature extraction.
- Develop HAR Models from Skeleton and Point Cloud Data: Design GNNs and CNNs specialized for HAR using skeleton data and point clouds, capturing temporal and spatial relationships.
- Comparison between models and report