Streamlining Video Analysis for Efficient Violence Detection

TL;DR

This paper introduces X3D, a 3D CNN-based model with pre-processing and clustering techniques, to improve automated violence detection in surveillance videos, enhancing security and content filtering applications.

Contribution

The paper presents a novel 3D CNN approach with specialized pre-processing and clustering for more accurate violence detection in videos.

Findings

Effective scene classification between fight and non-fight

Improved localization of violent scenes

Demonstrated robustness across diverse video datasets

Abstract

This paper addresses the challenge of automated violence detection in video frames captured by surveillance cameras, specifically focusing on classifying scenes as "fight" or "non-fight." This task is critical for enhancing unmanned security systems, online content filtering, and related applications. We propose an approach using a 3D Convolutional Neural Network (3D CNN)-based model named X3D to tackle this problem. Our approach incorporates pre-processing steps such as tube extraction, volume cropping, and frame aggregation, combined with clustering techniques, to accurately localize and classify fight scenes. Extensive experimentation demonstrates the effectiveness of our method in distinguishing violent from non-violent events, providing valuable insights for advancing practical violence detection systems.