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：本项贡献提出了TOMIE框架（多工业实体跟踪框架），用于通过一个由六个RGB摄像头组成的网络（以本示例为例）对工业实体（如托盘、板条箱、桶）进行连续跟踪。该框架整合了多传感器、数据处理流水线及数据标注流程，并在本文中进行了详细阐述。基于全自动工业实体跟踪系统的愿景，该框架使研究人员能够在工业环境下高效采集高质量数据。应用该框架构建了图像数据集——TOMIE数据集，并同时用于评估框架的有效性。该数据集包含来自覆盖大型室内空间的六台摄像头的112,860帧图像及640,936个实体实例的标注文件。其规模达到同类数据集的四倍，场景来源于仓储行业的工业应用。我们基于该数据集部署了三种跟踪算法——ByteTrack、Bot-Sort及SiamMOT，作为概念验证，并获得了与当前最优方法可比的跟踪结果。