Projects:Augment marker tracking with visual tracking

Overview

- Input : Video data, 2D location of birds , 3D trajectory (Labelled and unlabeled) - Output: A. Create 2D trajectories. Object detection on birds. (using simple blob tracking, or using machine learning) B. Match 3D - 2D trajectories to identify identity flips. C. Unlabeled trajectories can be combined with 2D trajectories, to fill gaps in 3D trajectories. (Project 2 output can be useful).

Subprojects: 3.1 Offline data processing; 3.2 Online/quasi-real time solution working from the data stream and video stream (video stream is not directly available in real time for processing, but realtime view is generated in the software, so this could be grabbed)

Contact

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Aims

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Estimated level of difficulty

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• Standard problems which just require applying existing methods (Hiwi level)
• Elaborate problems which require substantial adaptation or extension of existing methods (Master student level)
• Special problems which require research of entirely new methods and might lead to a paper or two (Ph.D. student level)

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Provided data

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Note: Once the CCU server is up and running, datasets should be stored there for easy availability. See the howtos on storage for details.

Suggested/tested approaches

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