Computer Vision III: Detection, Segmentation, and Tracking
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
IN2375 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
Content, Learning Outcome and Preconditions
- One-stage detectors (YOLO, SSD, RetinaNet)
- Point-based detection
- Instance segmentation (Mask-RCNN)
- Semantic segmentation
- Panoptic segmentation
- Video object segmentation (OSVOS)
- Visual object tracking
- Multiple object tracking
- Graph neural networks for object tracking
- 3D object tracking
- Trajectory prediction
MA0902 Analysis für Informatiker
MA0901 Lineare Algebra für Informatiker
Knowledge of Python and Pytorch is a must to complete the course practical assignments.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|Computer Vision III: Detection, Segmentation, and Tracking (IN2375)
Assistants: Elezi, I.
Thu, 14:00–16:00, virtuell
Tue, 10:00–12:00, virtuell
Learning and Teaching Methods
The practical sessions will allow the students to get familiar with state-of-the-art algorithms (implemented in PyTorch) and the tricks of trainings them for the aforementioned Computer Vision tasks.
Description of exams and course work
- Written test of 90 minutes at the end of the course.
- The students will be awarded a bonus in case they successfully complete all practical assignments. Their progress and/or final results will be presented in the form of a poster or an oral presentation.
The exam may be repeated at the end of the semester. There is a possibility to take the exam in the following semester.
Current exam dates
Currently TUMonline lists the following exam dates. In addition to the general information above please refer to the current information given during the course.
|Computer Vision III: Detection, Segmentation, and Tracking