Master’s Thesis: Applying SAM3 for Object Classification and Tracking in Mobile Visual Data

23.03.2026, Diplomarbeiten, Bachelor- und Masterarbeiten

This thesis explores the application of SAM3 to mobile video data for object classification and tracking. The focus is on evaluating SAM3’s performance, adaptability, and limitations in real-world mobile scenarios such as dynamic scenes, occlusions, and changing viewpoints.

Object classification and tracking are essential tasks in computer vision, with applications in robotics, surveillance, autonomous systems, and augmented reality. Recent advances in foundation models have led to powerful general-purpose vision systems capable of segmentation, tracking, and classification with minimal supervision.

Meta’s Segment Anything Model (SAM) and its newer version, SAM3, represent a major step forward in universal visual understanding. SAM3 is designed to perform robust segmentation and tracking across a wide variety of objects and environments, making it highly suitable for mobile vision tasks. When combined with mobile video data, SAM3 can enable flexible and efficient object understanding without task-specific retraining.

If you are interested in this thesis, please send your grades report and CV at the contact e-mail address.

Kontakt: tim.schreiter@tum.de

More Information

https://www.ce.cit.tum.de/pins/open-positions/student-positions/