Project Overview
Developed an advanced model capable of detecting, classifying, and counting vehicles in real time from live video feeds. This solution identifies multiple vehicle types—such as cars, trucks, and motorcycles—while providing accurate counts, empowering traffic management and urban planning efforts with actionable data.

Technologies Used

• Computer Vision
• Deep Learning (CNNs, YOLO, or SSD frameworks)
• Real-Time Video Processing
• Python, TensorFlow, OpenCV

Key Features

• Live vehicle detection and counting
• Differentiation between various vehicle categories
• Seamless integration with CCTV and traffic camera systems
• Real-time data dashboard for quick decision-making

Use Cases

• Urban traffic flow monitoring
• Smart traffic lights and adaptive signaling
• City infrastructure and planning analytics
• Automated vehicle counting for tolls or event management

Project Outcomes

• Enhanced efficiency in traffic regulation
• Data-driven urban planning and policy decisions
• Reduced congestion and improved road safety
• Reliable analytics for city management authorities

Challenges Overcome

• Maintaining high detection accuracy in diverse lighting and weather conditions
• Real-time processing for uninterrupted performance
• Vehicle differentiation in dense or crowded traffic scenes

Client/Industry

• Urban traffic authorities
• Smart city technology providers
• Traffic monitoring solution integrators

Our Role

• Full-cycle development: data collection, model training, video system integration, deployment, and maintenance