Abstract

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.

中文翻译：

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基于半自动计算机视觉的多个工业实体跟踪：框架和数据集创建方法

摘要

本贡献介绍了 TOMIE 框架（多个工业实体的跟踪），这是一个通过网络（在本例中为六个 RGB 摄像机）连续跟踪工业实体（例如托盘、板条箱、桶）的框架。该框架利用了多个传感器、数据管道和数据注释程序，并在本贡献中进行了详细描述。考虑到工业实体的全自动跟踪系统的愿景，它使研究人员能够在工业环境中有效捕获高质量的数据。使用该框架创建图像数据集 TOMIE 数据集，同时用于衡量框架的有效性。该数据集包含 112,860 个帧和 640,936 个实体实例的注释文件，这些注释文件是从感知大型室内空间的一组六个摄像机捕获的。该数据集的规模是同类数据集的四倍，并且由来自仓储领域工业应用的场景组成。该数据集应用了三种跟踪算法，即 ByteTrack、Bot-Sort 和 SiamMOT，作为概念验证并提供与现有技术相当的跟踪结果。