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Computer Vision III: Detection, Segmentation, and Tracking (IN2375)

Course 0000000435 in WS 2022/3

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 9 - Chair of Computer Vision and Artificial Intelligence (Prof. Cremers)
Lecturers Nikita Araslanov
Björn Häfner
Simon Weber
Responsible/Coordination: Daniel Cremers
Regine Hartwig
Dominik Muhle
Dates Tue, 16:00–18:00, Interims II 004
Thu, 08:00–10:00, MI HS1

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks - Proposal-based object detection (Faster-RCNN) - 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
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

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