This contribution presents the TOMIE framework (Tracking Of Multiple Industrial Entities), a framework for the continuous tracking of industrial entities (e.g., pallets, crates, barrels) over a network of, in this example, six RGB cameras. This framework, makes use of multiple sensors, data pipelines and data annotation procedures, and is described in detail in this contribution. With the vision of a fully automated tracking system for industrial entities in mind, it enables researchers to efficiently capture high quality data in an industrial setting. Using this framework, an image dataset, the TOMIE dataset, is created, which at the same time is used to gauge the framework's validity. This dataset contains annotation files for 112,860 frames and 640,936 entity instances that are captured from a set of six cameras that perceive a large indoor space. This dataset out-scales comparable datasets by a factor of four and is made up of scenarios, drawn from industrial applications from the sector of warehousing. Three tracking algorithms, namely ByteTrack, Bot-Sort and SiamMOT are applied to this dataset, serving as a proof-of-concept and providing tracking results that are comparable to the state of the art.
翻译:本文介绍了TOMIE框架(Tracking Of Multiple Industrial Entities),一种用于在六个RGB相机网络上连续跟踪工业实体(例如托盘、板条箱、桶)的框架。本框架利用多个传感器、数据管道和数据注释程序,并在本文中进行了详细描述。在实现工业实体全自动化跟踪系统的愿景下,它使研究人员能够有效地在工业环境中捕获高质量的数据。利用这个框架,创建了一个图像数据集--TOMIE数据集,同时用来评估框架的有效性。该数据集包含112,860帧的注释文件和640,936个实体实例,这些实例是从六个相机中捕获的,这些相机感知一个大型的室内空间。该数据集的规模比可比较的数据集大四倍,由来自仓储领域的工业应用场景组成。将三个跟踪算法,ByteTrack、Bot-Sort和SiamMOT应用于这个数据集,作为概念验证,并提供与现有技术相当的跟踪结果。