# Gun Detection Using Combined Human Pose and Weapon Appearance

arXiv: https://arxiv.org/abs/2503.12215 · HTML: https://mlsanity.com/paper/2503.12215/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 15 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Automatic Handgun Detection Alarm in Videos Using Deep Learning](https://mlsanity.com/paper/1702.05147/) (2017)
- [DynamicDet: A Unified Dynamic Architecture for Object Detection](https://mlsanity.com/paper/2304.05552/) (2023)
- [Handgun detection using combined human pose and weapon appearance](https://mlsanity.com/paper/2010.13753/) (2020)
- [Localizing Firearm Carriers by Identifying Human-Object Pairs](https://mlsanity.com/paper/2005.09329/) (2020)

## Built on by (most cited first, 1 of 1)

- [Confidence Aware SSD Ensemble with Weighted Boxes Fusion for Weapon Detection](https://mlsanity.com/paper/2509.23697/) (2025)

## Abstract (arXiv)

The increasing frequency of firearm-related incidents has necessitated advancements in security and surveillance systems, particularly in firearm detection within public spaces. Traditional gun detection methods rely on manual inspections and continuous human monitoring of CCTV footage, which are labor-intensive and prone to high false positive and negative rates. To address these limitations, we propose a novel approach that integrates human pose estimation with weapon appearance recognition using deep learning techniques. Unlike prior studies that focus on either body pose estimation or firearm detection in isolation, our method jointly analyzes posture and weapon presence to enhance detection accuracy in real-world, dynamic environments. To train our model, we curated a diverse dataset comprising images from open-source repositories such as IMFDB and Monash Guns, supplemented with AI-generated and manually collected images from web sources. This dataset ensures robust generalization and realistic performance evaluation under various surveillance conditions. Our research aims to improve the precision and reliability of firearm detection systems, contributing to enhanced public safety and threat mitigation in high-risk areas.