Images of Cylinders Transported on a Conveyor Belt - Recording 3

This data set comprises images of cylinders 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圆柱工件图像，所有图像均通过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以下文献：Georg Maier、Florian Pfaff、Christoph Pieper、Robin Gruna、Benjamin Noack、Harald Kruggel-Emden、Thomas Längle、Uwe D. Hanebeck、Jürgen Beyerer发表于《IEEE工业电子汇刊（Transactions on Industrial Electronics）》2020年2月的《Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking》，亦可访问项目官网获取更多信息。本数据集为光学分选机批量采集数据的一部分。可通过带引号的关键词"Tobias Hornberger"进行检索，或访问链接https://doi.org/10.5281/zenodo.5506551（仅包含传送带数据集）获取完整列表。本次采集使用的相机为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（debayer）脚本可于GitHub获取。在世界坐标系下，每个像素对应实际长度约0.056 mm，采集帧率为192.9 Hz。本数据集可用于开发与评估两类关键挑战对应的算法：一是预测颗粒运动的多目标跟踪算法，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：Florian Pfaff、Marcus Baum、Benjamin Noack、Uwe D. Hanebeck、Robin Gruna、Thomas Längle、Jürgen Beyerer发表于2015年IEEE智能系统多传感器融合与集成国际会议（MFI 2015）的《TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials》，会议于2015年9月在美国加利福尼亚州圣地亚哥举办；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月的《Predictive Tracking with Improved Motion Models for Optical Belt Sorting》。二是颗粒分类算法。分类过程可借助多目标跟踪器实时累积视觉特征，也可利用颗粒的轨迹信息完成分类。相关细节可参阅以下文献：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发表于第三届材料光学表征国际会议（OCM 2017）的《Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information》，会议于2017年3月在德国卡尔斯鲁厄举办；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）2017年10月的《Motion-Based Material Characterization in Sensor-Based Sorting》。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发表于2020年IEEE智能系统多传感器融合与集成国际会议（MFI 2020，线上举办）的《Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking》，会议于2020年9月线上召开。已完成关联的颗粒轨迹CSV文件可于https://doi.org/10.5281/zenodo.5506551获取。致谢 德国过程技术研究协会（Forschungs-Gesellschaft Verfahrens-Technik e.V., 简称GVT）的IGF 20354 N项目获得了资助：联邦经济事务与能源部依据德国联邦议会决议，通过工业共同体研究与发展（Industrial Community Research and Development, 简称IGF）资助计划下的AiF项目，为该项目提供了支持。