# Real-Time Threat Detection from Surveillance Cameras using Machine Learning

arXiv: https://arxiv.org/abs/2606.05708 · HTML: https://mlsanity.com/paper/2606.05708/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [A Dataset and System for Real-Time Gun Detection in Surveillance Video Using Deep Learning](https://mlsanity.com/paper/2105.01058/) (2021)
- [Real-Time Weapon Detection Using YOLOv8 for Enhanced Safety](https://mlsanity.com/paper/2410.19862/) (2024)

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

- [A Multi-Task Deep Learning Framework for Real-Time Intelligent Video Surveillance with Temporal Event Validation](https://mlsanity.com/paper/2607.03131/) (2026)

## Abstract (arXiv)

Ensuring public safety in densely populated urban environments remains a critical challenge, necessitating the deployment of intelligent and automated video surveillance systems. Traditional surveillance approaches rely heavily on manual monitoring, which is inefficient and susceptible to human fatigue, delayed response, and observational errors. To overcome these limitations, this work presents a real-time object detection-based surveillance framework. The proposed system focuses on detecting guns, knives, and region-specific blunt objects commonly involved in violent activities in Indian surveillance scenarios. A key contribution of this work is the use of a custom-created dataset collected using a mobile camera, consisting of 336 labeled images of blunt objects such as iron rods, wooden sticks, and plastic rods. This dataset is combined with a publicly available dataset of 7,623 images of guns and knives, forming a consolidated dataset of 7,959 images across three classes: gun, knife, and blunt object. The combined dataset is used to train a YOLOv8-based object detection model for real-time performance. Experimental evaluation shows that increasing the training duration significantly improves recall and average precision for the blunt object class without signs of overfitting. Overall, the proposed framework achieves an effective balance between accuracy and efficiency, making it suitable for deployment in real-world surveillance environments such as campuses, public spaces, and transportation areas.