AUTOMATIC VEHICLE COUNTING FOR TRAFFIC MANAGEMENT SYSTEM USING IMAGE
PROCESSING AND IoT
Abstract
Road traffic management is a critical component of intelligent
city administration. Traffic congestion may be efficiently addressed by
precisely calculating the number of vehicles likely to pass through a
busy intersection ahead of time. The system can predict vehicle counts
far before reaching the targeted traffic intersection by using image
processing and techniques. Furthermore, monitoring data may be
transferred through the internet to a remote-control hub situated
anywhere in the city. The technology integrates seamlessly with existing
traffic control systems, capturing vehicle pictures using strategically
placed cameras. These photos are then analyzed using image processing
techniques to count the cars properly. The data collected is sent to a
centralized administration system for real-time monitoring and traffic
analysis. Furthermore, the system optimizes traffic signal timings and
provides drivers with real-time information, resulting in considerable
congestion reduction, enhanced traffic flow, and important traffic
management insights.