Difference between revisions of "Projects:Automatic generation of optimal marker patterns"
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== Overview == | == Overview == | ||
| − | + | The Vicon Nexus software automatically identifies patterns of markers within the 3D point cloud of marker positions which is being tracked. To distinguish individuals, it is necessary to give a unique pattern to each individual, which the software can reliably detect and tell apart from the others. The key goal of this project is to establish an automated process to generate optimal versions of these patterns for different types of experiments. | |
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== Contact == | == Contact == | ||
| − | + | * Mate Nagy, mnagy@orn.mpg.de | |
== Aims == | == Aims == | ||
| − | + | * Generate unique patterns which can be optimally tracked and distinguished given the input design criteria (e.g. maximum number of markers, marker size, dimension of base plate, minimum distance between points). | |
| + | * In particular, controlled experiments need to be performed to find out what works well for the Vicon software systems, and what is required of a pattern for tracking to work well. | ||
| + | * Use VICON calibration to actually create a full 36 camera setup, simulate performance of these designs in terms of expected detection accuracy and performance in different areas of the barn. | ||
| + | * To reduce amount of manual work, it would be desirable to automatically produce precise tags (using CNC, laser cutting, 3D printing ...) based on a given pattern design. | ||
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* Special problems which require research of entirely new methods and might lead to a paper or two (Ph.D. student level) | * Special problems which require research of entirely new methods and might lead to a paper or two (Ph.D. student level) | ||
Maybe add a short clarification of what you believe are the main difficulties, and why you believe this is the right classification. | Maybe add a short clarification of what you believe are the main difficulties, and why you believe this is the right classification. | ||
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== Provided data == | == Provided data == | ||
| − | + | See [[Vicon:Data format documentation]]. | |
| − | + | This project requires a lot of experimentation with different marker setups and a number of controlled experiments, which need to be designed by whoever attacks it. | |
| − | == Suggested | + | == Suggested approaches == |
| − | + | Estimate 2D invariant values for various designs using theory of cross ratios and suggest creating individual patterns. Simulation of the data can be done using 1 or 2 virtual cameras. | |
Revision as of 09:40, 21 May 2019
Contents
Overview
The Vicon Nexus software automatically identifies patterns of markers within the 3D point cloud of marker positions which is being tracked. To distinguish individuals, it is necessary to give a unique pattern to each individual, which the software can reliably detect and tell apart from the others. The key goal of this project is to establish an automated process to generate optimal versions of these patterns for different types of experiments.
Contact
- Mate Nagy, mnagy@orn.mpg.de
Aims
- Generate unique patterns which can be optimally tracked and distinguished given the input design criteria (e.g. maximum number of markers, marker size, dimension of base plate, minimum distance between points).
- In particular, controlled experiments need to be performed to find out what works well for the Vicon software systems, and what is required of a pattern for tracking to work well.
- Use VICON calibration to actually create a full 36 camera setup, simulate performance of these designs in terms of expected detection accuracy and performance in different areas of the barn.
- To reduce amount of manual work, it would be desirable to automatically produce precise tags (using CNC, laser cutting, 3D printing ...) based on a given pattern design.
Estimated level of difficulty
If you have an estimate, classify level of difficulty according to the description of the CCU in the cluster proposal into
- 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)
Maybe add a short clarification of what you believe are the main difficulties, and why you believe this is the right classification.
Provided data
See Vicon:Data format documentation.
This project requires a lot of experimentation with different marker setups and a number of controlled experiments, which need to be designed by whoever attacks it.
Suggested approaches
Estimate 2D invariant values for various designs using theory of cross ratios and suggest creating individual patterns. Simulation of the data can be done using 1 or 2 virtual cameras.
