Images of Peppercorns Transported on a Conveyor Belt - Recording 12

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 <em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer,</em> <strong>Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking</strong>, 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<strong>.</strong> 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 <em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer,</em><br> <strong>TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials,</strong><br> Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), San Diego, California, USA, September 2015. <em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer,</em><br> <strong>Predictive Tracking with Improved Motion Models for Optical Belt Sorting</strong>,<br> 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 <em>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,</em><br> <strong>Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information,</strong><br> Proceedings of the 3rd Conference on Optical Characterization of Materials (OCM 2017), Karlsruhe, Germany, March 2017. <em>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,</em><br> <strong>Motion-Based Material Characterization in Sensor-Based Sorting,</strong> <br> tm – Technisches Messen, De Gruyter, October 2017. <br> To this date, publications that used these data include <em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck,</em><br> <strong>Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking</strong>,<br> 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. <strong>Acknowledgment</strong> 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花椒图像，图像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见<em>Georg Maier, Florian Pfaff, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, Thomas Längle, Uwe D. Hanebeck, Jürgen Beyerer</em>，<strong>《一种新型基于传感器的分选方法的实验评估：集成预测性实时多目标跟踪</strong>，发表于《IEEE Transactions on Industrial Electronics》2020年2月。亦可查阅项目官网获取更多信息。本数据集属于光学分选机录制的批次数据之一。请使用关键词"Tobias Hornberger"（带引号）进行检索，或通过https://doi.org/10.5281/zenodo.5506551获取相关列表（仅包含输送带数据集）。 采集图像所用相机为Bonito CL-400C。外参校准图像为calibration_extrinsics.png，色彩校准图像为calibration_color.png。相关去拜耳化脚本可于GitHub获取。世界坐标系下，每个像素的边长约为0.056 mm，帧率为192.9 Hz。 本数据集可用于开发并评估两项关键挑战的算法： 1. 多目标跟踪以预测颗粒运动：该算法可用于优化光学分选机的分选效果。相关细节可参阅以下文献： <em>Florian Pfaff, Marcus Baum, Benjamin Noack, Uwe D. Hanebeck, Robin Gruna, Thomas Längle, Jürgen Beyerer</em>，<strong>《TrackSort：用于分选非合作散装物料的预测性跟踪算法》</strong>，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。 <em>Florian Pfaff, Christoph Pieper, Georg Maier, Benjamin Noack, Robin Gruna, Harald Kruggel-Emden, Uwe D. Hanebeck, Siegmar Wirtz, Viktor Scherer, Thomas Längle, Jürgen Beyerer</em>，<strong>《基于改进运动模型的预测性跟踪在光学带式分选机中的应用》</strong>，发表于《– Automatisierungstechnik》2020年4月。 2. 颗粒分类：分类任务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文献： <em>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</em>，<strong>《利用运动信息优化基于传感器分选的材料表征精度》</strong>，发表于2017年第三届材料光学表征国际会议（OCM 2017），德国卡尔斯鲁厄，2017年3月。 <em>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</em>，<strong>《基于运动的传感器分选材料表征》</strong>，发表于《tm – Technisches Messen》，De Gruyter出版社，2017年10月。 截至目前，使用本数据集的相关研究包括： <em>Daniel Pollithy, Marcel Reith-Braun, Florian Pfaff, Uwe D. Hanebeck</em>，<strong>《循环神经网络在多目标跟踪应用中的不确定性估计》</strong>，发表于2020年IEEE多传感器融合与智能系统国际会议（MFI 2020），线上会议，2020年9月。 已完成关联的颗粒跟踪的CSV文件可于https://doi.org/10.5281/zenodo.5506551获取。 ### 致谢 本研究得到德国流程技术研究协会（Forschungs-Gesellschaft Verfahrens-Technik e.V.，简称GVT）的IGF项目20354 N资助，该项目由德国联邦经济事务与能源部通过AiF工业社区研究与发展计划（IGF）资助，资助依据为德国联邦议院决议。