Visual Behavior Analysis

Abstract: With rising crime rates and increasing security concerns, intelligent surveillance has become crucial for ensuring public safety.This paper presents a Visual Behavior Analysis system that automates crime detection and tracking using real-time video surveillance. The system employs You Only Look Once (YOLO) is a deep learning algorithm recognized for its speed and accuracy in multi-object detection, to monitor public spaces efficiently. By analysing movement patterns and identifying anomalies, it can detect potential threats and generate instant alerts to prevent criminal activities. Additionally, the system integrates facial recognition through the use of Haar Cascade classifiers to assist law enforcement in identifying individuals involved in suspicious activities. It also detects objects such as weapons or unusual postures, improving the accuracy of crime prediction. The system processes live video feeds, minimizing response time and reducing reliance on manual monitoring. By leveraging computer vision and artificial intelligence, improving security measures by offering proactive crime prevention and real-time situational awareness. It goes beyond just aids in immediate threat detection but also supports post-incident investigations by preserving crucial evidence. The proposed system provides a scalable, efficient, and automated solution for modern surveillance challenges, contributing to a safer and smarter security infrastructure.