An Overview of Violence Detection Techniques: Current Challenges and Future Directions

TL;DR

This paper reviews current violence detection techniques in video data, highlighting challenges, deep learning advancements, localization strategies, and future research directions for efficient and accurate analysis.

Contribution

It provides a comprehensive overview of deep sequence learning approaches, localization methods, and dataset analysis in violence detection, emphasizing efficiency and future challenges.

Findings

Deep learning models improve real-time violence detection accuracy.

Localization strategies enhance the precision of violence identification.

Analysis of datasets reveals gaps and future needs in VD research.

Abstract

The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), broadly plunging under Action and Activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally based on manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deep sequence learning approaches along with localization strategies of the detected violence. This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efficiency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from an in-depth analysis of the previous methods.