Real-time Weapon Detection in Videos

Ahmed Nazeem, Xinzhu Bei, Ruobing Chen, Shreyas Shrivastava

2022

Abstract

Real-time weapon detection in video is a challenging object detection task due to the small size of weapons relative to the image size. Thus, we try to solve the common problem that object detectors deteriorate dramatically as the object becomes smaller. In this manuscript, we aim to detect small-scale non-concealed rifles and handguns. Our contribution in this paper is (i) proposing a scale-invariant object detection framework that is particularly effective with small objects classification, (ii) designing anchor scales based on the effective receptive fields to extend the Single Shot Detection (SSD) model to take an input image of resolution 900*900, and (iii) proposing customized focal loss with hard-mining. Our proposed model achieved a recall rate of 86% (94% on rifles and 74% on handguns) with a false positive rate of 0.07% on a self-collected test set of 33K non-weapon images and 5K weapon images.

Paper Citation

in Harvard Style

Nazeem A., Bei X., Chen R. and Shrivastava S. (2022). Real-time Weapon Detection in Videos. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 497-504. DOI: 10.5220/0010699900003122

in Bibtex Style

@conference{icpram22,
author={Ahmed Nazeem and Xinzhu Bei and Ruobing Chen and Shreyas Shrivastava},
title={Real-time Weapon Detection in Videos},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={497-504},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010699900003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Real-time Weapon Detection in Videos
SN - 978-989-758-549-4
AU - Nazeem A.
AU - Bei X.
AU - Chen R.
AU - Shrivastava S.
PY - 2022
SP - 497
EP - 504
DO - 10.5220/0010699900003122