A client faced difficulties in accurately monitoring and counting various types of vehicles for their traffic management services. The existing system was inefficient, causing delays in decision-making and resource allocation. An accurate, automated solution was required to streamline operations and enhance traffic flow analysis.
Harrier Information Systems recommended a custom-built solution powered by Artificial Intelligence, Machine Learning, and Python.
By leveraging the OpenCV library for real-time video processing, the solution enabled precise vehicle tracking and counting as vehicles exited the frame.
The AI-driven model, developed to analyze various vehicle types, achieved a 75% accuracy rate, exceeding client expectations and improving the speed and reliability of traffic data collection.
Since implementation, the solution has delivered outstanding results:
• Enhanced vehicle tracking accuracy with minimal manual intervention.
• Improved operational efficiency, allowing for quicker, data-backed decisions for estimated toll collection and capital allocation for building infrastructure.
• A scalable framework adaptable to different traffic environments, offering long-term value.
The success of this project highlights the ability of Harrier Information Systems to design and deliver high-performing AI solutions that address complex business challenges. The solution continues to drive both operational improvements and scalable growth for the client.
Python, Machine Learning, AI, OpenCV