Case Study: Automated Traffic Analysis for Smart Cities

Introduction A system integrator responsible for several smart city projects in India needed an automated traffic analysis system to aid authorities in traffic management, law enforcement, and urban planning. They required a system that could count and identify different types of vehicles and detect number plates of law-breaking vehicles, using a live video feed of major intersections.

Problem Statement The main problem was to develop an automated traffic analysis system capable of counting and identifying different types of vehicles, detecting number plates of violating vehicles, and alerting authorities if any violations were detected. The system needed to process a live video feed from major intersections, analyze the data in real-time, and generate reports for authorities.

Solution Approach A deep learning-based solution was implemented using the yolov4 model, which achieved a counting accuracy of 99% and classification accuracy of around 95%. The system was designed to analyze a live video feed of major intersections, count and identify the types of vehicles, and detect number plates of violating vehicles. The solution was based on open-source tools and techniques and was designed to work offline as well. The system could analyze 2 frames per second and display results on a monitoring screen in real-time.

The solution involved several key steps:

Data Collection: The system collected data from a live video feed of major intersections, captured using CCTV cameras installed at different points across the intersection.

Object Detection: The system used the yolov4 model to detect and classify vehicles in the video feed. The model was trained on a large dataset of vehicles and was capable of detecting different types of vehicles with high accuracy.

Number Plate Detection: The system used optical character recognition (OCR) to detect number plates of violating vehicles. The OCR engine was trained on a large dataset of number plates and was capable of detecting number plates with high accuracy.

Data Analysis and Visualization The system analyzed the data collected from the video feed and generated reports that could be used by authorities for traffic management, law enforcement, and urban planning.

Results The automated traffic analysis system was successfully implemented and tested at major intersections in the smart city. The system achieved a counting accuracy of 99% and classification accuracy of around 95%. The system could analyze 2 frames per second and display results on a monitoring screen in real-time. The system was also designed to work offline, ensuring that data analysis and reporting could continue even during a network outage.

Conclusion The automated traffic analysis system developed by the system integrator provided authorities with a powerful tool for traffic management, law enforcement, and urban planning. The solution was based on open-source tools and techniques, making it cost-effective and scalable. The system's high accuracy and real-time analysis capabilities helped authorities make data-driven decisions, leading to better traffic management and urban planning.