REAL TIME VIOLENCE DETECTION

Abstract

Real-time violence detection has become increasingly essential in today's security and surveillance
systems. This paper proposes a novel approach utilizing advanced computer vision techniques and
machine learning algorithms for the real-time detection of violent behavior in video streams. By extracting
key features such as motion patterns, body poses, and spatial relationships, coupled with deep learning
models for classification, our system achieves high accuracy and efficiency in identifying violent acts as
they occur. The proposed framework offers promising potential for enhancing public safety, facilitating
timely interventions, and mitigating potential threats in various real-world scenarios.