DIFEM: Key-points Interaction based Feature Extraction Module for Violence Recognition in Videos

TL;DR

This paper introduces DIFEM, a lightweight and effective feature extraction module that uses human skeleton key-points to improve violence recognition in videos, outperforming existing methods.

Contribution

The paper presents a novel Dynamic Interaction Feature Extraction Module (DIFEM) that captures violence-related motion features efficiently, with fewer parameters than deep learning models.

Findings

DIFEM effectively captures motion dynamics like velocity and joint intersections.

The method outperforms several state-of-the-art violence recognition techniques.

Experiments on three datasets demonstrate promising results across all.

Abstract

Violence detection in surveillance videos is a critical task for ensuring public safety. As a result, there is increasing need for efficient and lightweight systems for automatic detection of violent behaviours. In this work, we propose an effective method which leverages human skeleton key-points to capture inherent properties of violence, such as rapid movement of specific joints and their close proximity. At the heart of our method is our novel Dynamic Interaction Feature Extraction Module (DIFEM) which captures features such as velocity, and joint intersections, effectively capturing the dynamics of violent behavior. With the features extracted by our DIFEM, we use various classification algorithms such as Random Forest, Decision tree, AdaBoost and k-Nearest Neighbor. Our approach has substantially lesser amount of parameter expense than the existing state-of-the-art (SOTA) methods employing deep learning techniques. We perform extensive experiments on three standard violence recognition datasets, showing promising performance in all three datasets. Our proposed method surpasses several SOTA violence recognition methods.