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.