Images of Peppercorns Transported on a Conveyor Belt - Recording 5

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.

本数据集包含传送带上的胡椒粒图像，所有图像均由小型光学带式分选机Tablesort采集。关于Tablesort系统的详细说明，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工业电子学报（Transactions on Industrial Electronics）》2020年2月刊的论文《Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking》，另可查阅项目官网获取相关信息。本数据集属于光学分选机采集的一批录制数据之一，可通过带引号的关键词"Tobias Hornberger"进行检索，或访问https://doi.org/10.5281/zenodo.5506551获取相关列表（仅包含传送带数据集）。图像采集采用Bonito CL-400C型号相机，外参校准图像为calibration_extrinsics.png，色彩校准图像为calibration_color.png；相关去拜耳脚本可在GitHub平台获取。世界坐标系下，每个像素对应实际长度约0.056 mm，帧率为192.9 Hz。本数据集可用于开发并评估两类核心挑战对应的算法：一是用于预测颗粒运动的多目标跟踪（Multi-Target Tracking）算法，该算法可用于提升光学分选机的分选性能，相关细节可参阅以下论文：Florian Pfaff、Marcus Baum、Benjamin Noack、Uwe D. Hanebeck、Robin Gruna、Thomas Längle、Jürgen Beyerer的《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；以及Florian Pfaff、Christoph Pieper、Georg Maier、Benjamin Noack、Robin Gruna、Harald Kruggel-Emden、Uwe D. Hanebeck、Siegmar Wirtz、Viktor Scherer、Thomas Längle、Jürgen Beyerer发表于《Automatisierungstechnik》2020年4月刊的《Predictive Tracking with Improved Motion Models for Optical Belt Sorting》。二是颗粒分类算法，颗粒分类可借助多目标跟踪器随时间累积视觉特征，也可利用轨迹信息完成颗粒分类，相关细节可参阅以下论文：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的《Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information》，收录于第三届材料光学表征国际会议（OCM 2017）论文集，德国卡尔斯鲁厄，2017年3月；以及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发表于《tm – Technisches Messen》（De Gruyter出版社）2017年10月刊的《Motion-Based Material Characterization in Sensor-Based Sorting》。截至目前，使用本数据集的已发表研究包括：Daniel Pollithy、Marcel Reith-Braun、Florian Pfaff、Uwe D. Hanebeck的《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，虚拟会议，2020年9月。已完成颗粒轨迹关联的CSV文件可通过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.，缩写GVT）的IGF项目20354 N资助，该项目由德国应用研究促进协会（AiF）通过工业共同体研究与发展（IGF）计划提供支持，资助方为德国联邦经济事务与能源部，资助依据为德国联邦议院决议。