Project Overview
Implemented a robust deep learning model that automatically detects traffic accidents in video footage. This system analyzes both real-time and recorded videos to accurately identify potential accidents, enabling authorities to respond quickly, improve road safety, and reduce emergency response times.

Technologies Used

• Deep Learning (Accident Detection Models)
• Computer Vision
• Real-Time Video Analytics
• Python, TensorFlow, OpenCV, CNNs

Key Features

• Automated accident detection from live or archived traffic footage
• Immediate alerting and event notification
• Evidence capture for post-incident analysis
• Seamless integration with city surveillance and traffic monitoring networks

Use Cases

• Rapid emergency response and incident dispatch
• Continuous road safety monitoring
• Accident data collection for analytics and prevention
• Law enforcement and insurance claim verification

Project Outcomes

• Reduced incident response times
• Enhanced road safety and accident prevention
• Reliable, unbiased evidence for investigations
• Improved resource allocation for emergency services

Challenges Overcome

• Accurate accident recognition in varied traffic and weather conditions
• Minimizing false alarms in diverse real-world scenarios
• Real-time processing of high-throughput video streams

Client/Industry

• Traffic management authorities
• Emergency response agencies
• Law enforcement
• Insurance companies

Our Role

• End-to-end solution delivery: model design, data training, integration, deployment, and ongoing system support