Images of Balls Transported on a Conveyor Belt - Recording 5

This data set comprises images of balls 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.本数据集收录了传送带上物料球体的图像，采集自小型光学带式分拣机Tablesort。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合著的论文《Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking》，发表于2020年2月的《IEEE Transactions on Industrial Electronics（IEEE工业电子汇刊）》；另可访问项目官网获取相关信息。 本数据集属于光学分拣机批量采集图像集的一部分。可通过带引号的关键词"Tobias Hornberger"检索获取相关数据集，或访问https://doi.org/10.5281/zenodo.5506551获取仅包含传送带数据集的列表。 图像采集使用Bonito CL-400C相机。外参校准图像可于calibration_extrinsics.png文件中获取，色彩校准图像则存储于calibration_color.png；相关去拜耳（debayer）脚本可参阅GitHub平台。 世界坐标系下，每个像素对应实际尺寸约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核心挑战对应的算法： 1. 多目标跟踪（Multitarget Tracking）算法，用于预测物料运动轨迹，该技术可有效提升光学分拣机的分拣性能。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合著的《TrackSort: Predictive Tracking for Sorting Uncooperative Bulk Materials》，发表于2015年9月于美国加利福尼亚州圣地亚哥举办的2015年IEEE多传感器融合与智能系统集成国际会议（2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 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合著的《Predictive Tracking with Improved Motion Models for Optical Belt Sorting》，发表于2020年4月的《Automatisierungstechnik（自动化技术）》。 2. 物料颗粒分类算法。该分类任务可借助多目标跟踪器随时间累积视觉特征，也可利用物料运动轨迹信息实现分类。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合著的《Improving Material Characterization in Sensor-Based Sorting by Utilizing Motion Information》，发表于2017年3月于德国卡尔斯鲁厄举办的第3届材料光学表征会议（3rd Conference on Optical Characterization of Materials, OCM 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合著的《Motion-Based Material Characterization in Sensor-Based Sorting》，发表于2017年10月的《tm – Technisches Messen（技术测量）》（De Gruyter出版）。 截至目前，已使用本数据集的研究包括：Daniel Pollithy、Marcel Reith-Braun、Florian Pfaff、Uwe D. Hanebeck合著的《Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking》，发表于2020年9月线上举办的2020年IEEE多传感器融合与智能系统集成国际会议（2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020）。 已完成关联的物料轨迹CSV文件可于https://doi.org/10.5281/zenodo.5506551获取。 致谢 本研究受德国联邦经济事务与能源部基于德国联邦议会决议通过的工业共同体研究与发展（IGF）资助计划支持，具体为Forschungs-Gesellschaft Verfahrens-Technik e.V.（GVT）协会的IGF项目20354 N，由AiF提供资助。