Real-Time Threat Identification: a Video Analytics-Based Violence Detection System

EasyChair Preprint 15808

Abstract

The task of detecting violence is crucial and has significant repercussions for both public safety and societal well-being. Because real-world settings are dynamic, traditional methods frequently find it difficult to adjust, which has led to the investigation of new computational techniques. This study exam-ines modern approaches to violence detection by utilizing knowledge from multidisciplinary and artificial intelligence research. By combining computer vision, signal processing, and behavioral psychology, we offer a comprehen-sive framework that can be used to recognize and classify violent incidents in a variety of settings. We investigate the effectiveness of cutting-edge methods, such Long Short-Term Memory (LSTM) networks, in identifying minor behavioral indicators that suggest aggression and capturing temporal correlations using real-world datasets.

Keyphrases: LSTM, Violence Detection, anomaly detection, deep learning, surveillance

@booklet{EasyChair:15808,
  author    = {Ajay Talele and Jyoti Kanjalkar and Vaishnavi Gosavi and Revati Nimbalkar and Vaishnavi Patade and Shrusti Gavali and Akhilesh Pimple},
  title     = {Real-Time Threat Identification: a Video Analytics-Based Violence Detection System},
  howpublished = {EasyChair Preprint 15808},
  year      = {EasyChair, 2025}}