Images of Balls Transported on a Conveyor Belt - Recording 6

This data set comprises images of balls on a conveyor belt. The images were recorded on the small-scale optical belt sorter Tablesort. A thorough description of the Tablesort system can be found in

• Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer, Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking, Transactions on Industrial Electronics, February 2020.
• See also the project website.

This dataset is part of a batch of recordings on optical sorters. Please use the search function with the keyword "Tobias Hornberger" (in quotes) to find them or use the list at https://doi.org/10.5281/zenodo.5506551 (conveyor belt data sets only).

The camera was recorded on a Bonito CL-400C. The calibration image for the extrinsic parameters can be found in calibration_extrinsics.png for the extrinsics and calibration_color.png for the color calibration. Please see the debayer script on GitHub. Each pixel is approximately 0.056 mm long in world coordinates. The frame rate is 192.9 Hz.

Algorithms for two key challenges can be developed and evaluated on the data sets:

1. Multitarget tracking for predicting the particle’s motion. This can be used to enhance the separation of optical sorters. For further details on this, see the publications
   • Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,
     TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,
     Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015.
   • Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,
     Predictive Tracking with Improved Motion Models for Optical Belt Sorting,
     at – Automatisierungstechnik, April 2020.
2. Classification of particles. The classification may use a multitarget tracker to accumulate visual features over time. One can also use the information on the trajectory to classify the particles. For information on this, refer to
   • Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,
     Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,
     Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017.
   • Georg Maier, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Jürgen Beyerer,
     Motion-Based Material Characterization in Sensor-Based Sorting, 
     tm – Technisches Messen, De Gruyter, October 2017.

To this date, publications that used these data include

• Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,
  Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking,
  Proceedings of the 2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2020), Virtual, September 2020.

CSV-files with already associated particle tracks are available at https://doi.org/10.5281/zenodo.5506551.

Acknowledgment

The IGF project 20354 N of the research association Forschungs-Gesellschaft Verfahrens-Technik e.V. (GVT) was supported via the AiF in a program to promote the Industrial Community Research and Development (IGF) by the Federal Ministry for Economic Affairs and Energy on the basis of a resolution of the German Bundestag.