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Related Experiment Video

Updated: Nov 25, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

A dataset for automatic violence detection in videos.

Miriana Bianculli¹, Nicola Falcionelli², Paolo Sernani²

¹Università degli Studi di Roma La Sapienza, Piazzale Aldo Moro 5, Roma 00185, Italy.

|December 15, 2020

Summary

This study introduces a new dataset of 350 high-resolution video clips to improve automatic violence detection systems. The dataset helps reduce false positives by including non-violent actions that mimic violent behaviors.

Keywords:

Computer vision Crime detection Deep learning Violence detection

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Automatic violence detection in videos is crucial for security, reducing manual surveillance workload.
Existing datasets often lack high resolution or are too specific, limiting robustness testing.
There is a need for datasets that challenge violence detection algorithms with non-violent actions causing false positives.

Purpose of the Study:

To introduce a novel, high-resolution dataset for evaluating the robustness of violence detection algorithms.
To address the issue of false positives in violence detection caused by similar-looking non-violent behaviors.
To provide a benchmark for testing the accuracy and reliability of automated security systems.

Main Methods:

A dataset of 350 video clips (1920x1080 pixels, 30 fps) was created.
Clips were labeled as either violent (230 clips) or non-violent (120 clips).
Non-violent clips feature actions like hugs and exultations that can be misclassified as violent due to motion and similarity.

Main Results:

The dataset comprises 350 high-resolution video clips designed for violence detection research.
It specifically includes challenging non-violent actions to test for false positives.
The dataset features performances by 2-4 non-professional actors per clip.

Conclusions:

The proposed dataset enhances the evaluation of violence detection systems by including realistic false positive scenarios.
It facilitates the development of more robust and accurate automated security solutions.
This resource aids researchers in improving algorithms to distinguish between genuine violence and similar non-violent actions.