Semi-Automated Computer Vision based Tracking of Multiple Industrial Entities -- A Framework and Dataset Creation Approach

TL;DR

This paper introduces the TOMIE framework for semi-automated multi-entity tracking in industrial environments, along with a large-scale dataset and evaluation of existing tracking algorithms, advancing industrial computer vision applications.

Contribution

The paper presents a comprehensive framework and a large annotated dataset for tracking multiple industrial entities, facilitating research and development in automated industrial monitoring.

Findings

The TOMIE dataset contains over 112,860 frames and 640,936 entity instances.

The dataset out-scales comparable datasets by a factor of four.

Existing tracking algorithms achieve results comparable to state-of-the-art methods on this dataset.

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.