FiDaSS: A Novel Dataset for Firearm Threat Detection in Real-World Scenes

Murilo Regio, Isabel Manssour

2025

Abstract

For a society to thrive, people must feel safe; otherwise, fear and stress reduce the quality of life. A variety of security measures are used, but as populations grow and firearms become more accessible, societal safety faces new challenges. Existing works on threat detection focus primarily on security cameras but lack common benchmarks, standard datasets, or consistent constraints, making it difficult to assess their real-world performance, especially with low-quality footage. This work introduces a challenging dataset for Firearm Threat Detection, comprising 7450 annotated frames across 291 videos, created under rigorous quality controls. We also developed tools to streamline dataset creation and expansion through semi-automatic annotations. To our knowledge, this is the largest real-world dataset with frame-level annotations in the area. Our dataset is available online alongside the tools developed, including some to facilitate its extension. We evaluated popular detectors and state-of-the-art transformer-based methods on the dataset to validate its difficulty.

Paper Citation

in Harvard Style

Regio M. and Manssour I. (2025). FiDaSS: A Novel Dataset for Firearm Threat Detection in Real-World Scenes. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 683-690. DOI: 10.5220/0013177800003912

in Bibtex Style

@conference{visapp25,
author={Murilo Regio and Isabel Manssour},
title={FiDaSS: A Novel Dataset for Firearm Threat Detection in Real-World Scenes},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={683-690},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013177800003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - FiDaSS: A Novel Dataset for Firearm Threat Detection in Real-World Scenes
SN - 978-989-758-728-3
AU - Regio M.
AU - Manssour I.
PY - 2025
SP - 683
EP - 690
DO - 10.5220/0013177800003912
PB - SciTePress