# Localizing Firearm Carriers by Identifying Human-Object Pairs

arXiv: https://arxiv.org/abs/2005.09329 · HTML: https://mlsanity.com/paper/2005.09329/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

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

- [Gun Detection Using Combined Human Pose and Weapon Appearance](https://mlsanity.com/paper/2503.12215/) (2025)
- [MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance](https://mlsanity.com/paper/2104.11653/) (2021)
- [Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures](https://mlsanity.com/paper/2309.09236/) (2023)

## Abstract (arXiv)

Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes. In a given image, human and firearms are separately detected. Each detected human is paired with each detected firearm, allowing us to create a paired bounding box that contains both object and the human. A network is trained to classify these paired-bounding-boxes into human carrying the identified firearm or not. Extensive experiments were performed to evaluate effectiveness of the algorithm, including exploiting full pose of the human, hand key-points, and their association with the firearm. The knowledge of spatially localized features is key to success of our method by using multi-size proposals with adaptive average pooling. We have also extended a previously firearm detection dataset, by adding more images and tagging in extended dataset the human-firearm pairs (including bounding boxes for firearms and gunmen). The experimental results (APhold = 78.5) demonstrate effectiveness of the proposed method.