Event-based dataset for the detection and classification of manufacturing assembly tasks
<|reference_start|>Event-based dataset for the detection and classification of manufacturing assembly tasks: 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.<|reference_end|>
@article{duarte2024event-based,
    title={Event-based dataset for the detection and classification of
  manufacturing assembly tasks},
    author={Laura Duarte, Pedro Neto},
    journal={Data in Brief, Volume 54, 2024, 110340, ISSN 2352-3409},
    year={2024},
    doi={10.1016/j.dib.2024.110340},
    archivePrefix={arXiv},
    eprint={2405.14626},
    primaryClass={cs.CV}
}