Real-time Violence Detection System Using Yolov11 And Deep Learning

Abstract

The Widespread Deployment Of Surveillance Systems Has Resulted In Continuous Generation Of Large-scale Video Data, Creating Challenges In Timely Identification Of Violent Incidents Such As Physical Assaults And Aggressive Behavior. Conventional Monitoring Approaches That Rely On Manual Observation Or Rule-based Methods Often Suffer From Delays, Inaccuracies, And High Dependency On Human Intervention. To Address These Limitations, This Research Presents A Real-time Violence Detection Framework Based On Advanced Deep Learning Techniques. The Proposed Approach Employs The YOLOv11 Algorithm For Rapid Object Detection, Enabling Efficient Identification Of Weapons And Suspicious Activities In Video Streams. Extracted Frames Are Processed Through Convolutional Neural Networks To Capture Contextual And Behavioral Features Associated With Violent Actions. The Integration Of Spatial And Temporal Analysis Enhances The System’s Capability To Differentiate Between Normal And Abnormal Human Behavior, Thereby Reducing False Alarms. Upon Detection Of Potential Threats, The System Generates Real-time Alerts Containing Essential Information Such As Timestamps, Detected Objects, And Confidence Levels. This Facilitates Prompt Response From Security Personnel And Improves Overall Situational Awareness. The Research Demonstrates An Effective And Scalable Solution For Automated Surveillance, Contributing To Enhanced Public Safety And Reliable Monitoring In Complex Environments.

Keywords