Images of Peppercorns Transported on a Conveyor Belt - Recording 2

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。所有图像均通过小型光学带式分选机Tablesort采集完成。关于Tablesort系统的详细说明可参阅以下文献：Georg Maier、Florian Pfaff、Christoph Pieper、Robin Gruna、Benjamin Noack、Harald Kruggel-Emden、Thomas Längle、Uwe D. Hanebeck、Jürgen Beyerer所著《Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking》，发表于《Transactions on Industrial Electronics》2020年2月刊。另可参阅项目官网。本数据集属于光学分选机录制数据集批次的一部分。请使用搜索功能，以带引号的关键词"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）。图像采集使用Bonito CL-400C相机。外参校准图像为calibration_extrinsics.png，色彩校准图像为calibration_color.png。关于去拜耳化脚本，请参阅GitHub上的相关仓库。世界坐标系下，每个像素对应实际长度约0.056 mm，图像帧率为192.9 Hz。本数据集可用于开发并评估两类关键挑战的算法： 1. 多目标跟踪：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。相关详细说明可参阅以下两篇文献： - Florian Pfaff、Marcus Baum、Benjamin Noack、Uwe D. Hanebeck、Robin Gruna、Thomas Längle、Jürgen Beyerer所著《TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials》，发表于2015年IEEE多传感器融合与智能系统国际会议（MFI 2015），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所著《Predictive Tracking with Improved Motion Models for Optical Belt Sorting》，发表于《Automatisierungstechnik》2020年4月刊。 2. 颗粒分类：可借助多目标跟踪器累积随时间变化的视觉特征，也可利用颗粒轨迹信息完成分类。相关详细说明可参阅以下两篇文献： - 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所著《Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information》，发表于第三届材料光学表征会议（OCM 2017），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所著《Motion-Based Material Characterization in Sensor-Based Sorting》，发表于《tm – Technisches Messen》（De Gruyter出版）2017年10月刊。 截至目前，使用本数据集的已发表文献包括：Daniel Pollithy、Marcel Reith-Braun、Florian Pfaff、Uwe D. Hanebeck所著《Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking》，发表于2020年IEEE多传感器融合与智能系统国际会议（MFI 2020），虚拟会议，2020年9月。 已完成轨迹关联的颗粒跟踪CSV文件可在https://doi.org/10.5281/zenodo.5506551获取。 致谢：本研究所属的IGF项目20354 N由德国过程技术研究协会（Forschungs-Gesellschaft Verfahrens-Technik e.V.，简称GVT）发起，获德国联邦经济事务与能源部依据德国联邦议会决议，通过AiF资助的工业共同体研发（Industrial Community Research and Development, IGF）计划支持。