INTELLIGENT ACCIDENT ALERT SYSTEM USING AI FOR TRAFFIC SURVEILLANCE VIDEOS

Abstract

Accident detection from CCTV footage is a vital advancement in computer vision, contributing significantly to public safety. Road and public area accidents can have serious consequences, making timely detection crucial for rapid emergency response and potential life-saving interventions. Traditional detection systems primarily relied on motion detection algorithms and predefined rules to identify incidents. These systems triggered alarms when unusual activity was detected, but they were often prone to false positives and lacked the ability to differentiate between different types of incidents accurately. Additionally, manual monitoring proved inefficient, as it required constant human supervision, leading to potential delays in response times. With advancements in machine learning, particularly convolutional neural networks (CNNs), the process of accident detection has become more automated and precise. Machine learning models can recognize complex patterns and features within images and video sequences, making them highly effective for accident detection tasks. The increasing number of accidents and the demand for swift emergency responses have highlighted the necessity for automated solutions. Manual monitoring is not only resource-intensive but also prone to human error, especially when attention is divided across multiple screens and locations. Automated accident detection using machine learning enables real-time monitoring, significantly improving response efficiency. This research presents a customized approach to detecting accidents from CCTV surveillance footage using machine learning techniques. By replacing traditional rulebased methods with intelligent, data-driven algorithms, the system enhances accuracy and reduces false alarms. The integration of machine learning for accident detection revolutionizes public safety by ensuring immediate alerts and swift emergency responses, ultimately contributing to safer roads and public spaces