# Event-based dataset for the detection and classification of manufacturing assembly tasks

arXiv: https://arxiv.org/abs/2405.14626 · HTML: https://mlsanity.com/paper/2405.14626/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 2 of 2)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Classification of Primitive Manufacturing Tasks from Filtered Event Data](https://mlsanity.com/paper/2303.09558/) (2023)

## Built on by (most cited first, 3 of 3)

- [DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition](https://mlsanity.com/paper/2407.05106/) (2024)
- [MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection](https://mlsanity.com/paper/2505.11282/) (2025)
- [Ensemble Methods for Sequence Classification with Hidden Markov Models](https://mlsanity.com/paper/2409.07619/) (2024)

## Abstract (arXiv)

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.