Projects:Automatic optimization of VICON system and recording parameters

Overview

The aim is to find the optimal setting of the parameters of the Vicon system based on the problem which needs to be solved. For example, if the animals move very fast or slow, or if their motion is mainly on the surface vs. using the full 3D, or based on the different marker sizes and pattern designs.

Currently, the system has 3 different types of parameters:

1. things that needs to be set by adjusting the apparatus (focusing and aperture of the cameras, their directions, etc) (Note: we may not want to change this)
2. parameters that can be set digitally from the Vicon software before recording and that cannot be changed later (store intensity, parameters for resolving overlapping blobs, etc.)
3. parameters that can be set digitally and affect the Vicon's software tracking that can be change later to re-run the analysis pipeline

In both category (2) and (3) there are around 5-10 important parameters. Finding the optimal setting is very hard doing this manually. Solving challenge (3) could be done offline, so it is an easier task. (2) requires actual measurements there. We have a robot vacuum cleaner that goes around in the full area, and can be used for testing. So if there is an algorithm that sets different parameters in the software and robots goes around after the algorithm evaluates the tracking output and changes the parameters until it finds an optimal setting.

Contact

• Mate Nagy, mnagy@orn.mpg.de

Aims

List the aims of your project, or what you expect anyone taking up the project is supposed to hopefully achieve. The more specific, the better.

Estimated level of difficulty

This seems like an elaborate problem, which would make an interesting Master's thesis (or three).

Provided data

Give a specific description of the datasets you provide or can provide which people need to use to solve your problem. If available and/or necessary, also suggest some means for reading the data format. If you can provide links to the data so people can download an take a look, all the better. Also list any known limitations, whether you can easily acquire/record new data, or any other useful information.

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 approaches

Suggested things which could be tried out on subproject (1):

• Input :Use the .xcp and image error , world error criteria to judge calibration quality.
• Output: Estimate the cameras or areas that would perform poorly due to bias in calibration.