Images of Peppercorns Transported on a Conveyor Belt - Recording 3

This data set comprises images of peppercorns 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: 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. 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.

本数据集包含输送带上的胡椒粒图像，采集于Tablesort型小型光学带式分选机。关于Tablesort系统的详尽说明，可参阅Georg Maier、Florian Pfaff、Christoph Pieper、Robin Gruna、Benjamin Noack、Harald Kruggel-Emden、Thomas Längle、Uwe D. Hanebeck、Jürgen Beyerer发表于《IEEE Transactions on Industrial Electronics》2020年2月的论文《Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking》。另可访问项目官网获取更多信息。

本数据集属于光学分选机录制的一批数据合集。请以关键词“Tobias Hornberger”（带引号）使用检索功能查找相关数据，或访问https://doi.org/10.5281/zenodo.5506551（仅包含输送带数据集）获取对应列表。

图像采集使用的相机为Bonito CL-400C。外参标定图像为calibration_extrinsics.png，色彩标定图像为calibration_color.png。相关去拜耳脚本可在GitHub平台查看。世界坐标系下，每个像素的实际尺寸约为0.056 mm，采集帧率为192.9 Hz。

本数据集可用于开发并评估两类核心挑战的算法：
1. 多目标跟踪（Multitarget Tracking）：用于预测颗粒运动轨迹，可用于提升光学分选机的分选性能。相关细节可参阅以下两篇论文：
- Florian Pfaff、Marcus Baum、Benjamin Noack、Uwe D. Hanebeck、Robin Gruna、Thomas Längle、Jürgen Beyerer，《TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials》，收录于2015年IEEE多传感器融合与智能系统国际会议（MFI 2015）论文集，美国加利福尼亚州圣地亚哥，2015年9月。
- 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》，发表于《Automatisierungstechnik》2020年4月。
2. 颗粒分类：可借助多目标跟踪器随时间累积视觉特征，也可利用颗粒的轨迹信息完成分类任务。相关细节可参阅以下两篇论文：
- 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》，收录于第三届材料光学表征会议（OCM 2017）论文集，德国卡尔斯鲁厄，2017年3月。
- 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》，发表于《tm – Technisches Messen》（De Gruyter出版社）2017年10月。

截至目前，使用本数据集的已发表论文包括：Daniel Pollithy、Marcel Reith-Braun、Florian Pfaff、Uwe D. Hanebeck，《Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking》，收录于2020年IEEE多传感器融合与智能系统国际会议（MFI 2020）论文集，线上举办，2020年9月。

附带已完成关联的颗粒轨迹的CSV文件可访问https://doi.org/10.5281/zenodo.5506551获取。

致谢
本研究得到德国工艺技术研究协会（Forschungs-Gesellschaft Verfahrens-Technik e.V.，简称GVT）的IGF项目20354 N资助，该项目经由工业共同体研究与发展（IGF）计划下的AiF提供支持，资助方为德国联邦经济事务与能源部，依据德国联邦议院的决议立项。