The featured dataset, the Event-based Dataset of Assembly Tasks (EDAT24),
showcases a selection of manufacturing primitive tasks (idle, pick, place, and
screw), which are basic actions performed by human operators in any
manufacturing assembly. The data were captured using a DAVIS240C event camera,
an asynchronous vision sensor that registers events when changes in light
intensity value occur. Events are a lightweight data format for conveying
visual information and are well-suited for real-time detection and analysis of
human motion. Each manufacturing primitive has 100 recorded samples of
DAVIS240C data, including events and greyscale frames, for a total of 400
samples. In the dataset, the user interacts with objects from the open-source
CT-Benchmark in front of the static DAVIS event camera. All data are made
available in raw form (.aedat) and in pre-processed form (.npy). Custom-built
Python code is made available together with the dataset to aid researchers to
add new manufacturing primitives or extend the dataset with more samples.

这篇研究论文描述了一个名为 “Event-based Dataset of Assembly Tasks (EDAT24)” 的特色数据集，该数据集展示了一系列制造基本任务（空闲、取物、放置和拧紧），这些任务是任何制造装配中人工操作员执行的基本动作。对于实时检测和分析人体运动，该数据集使用了 DAVIS240C 事件相机，一种异步视觉传感器，当光强度发生变化时会注册事件。数据集中包含了使用 CT-Benchmark 开源项目的对象与静态 DAVIS 事件相机进行交互的行为样本，数据以原始格式（.aedat）和预处理格式（.npy）提供，并且还附带了自定义的 Python 代码，以帮助研究人员添加新的制造基本任务或扩展数据集的样本数量。