The Density Based Traffic Monitoring System through IoT using ResNet is an intelligent framework designed to optimize traffic management and reduce congestion in urban areas through automated analysis of vehicular flow. Traditional traffic control systems rely on fixed signal timing, which often leads to unnecessary delays and traffic jams, especially during peak hours. To overcome these limitations, this system integrates Internet of Things (IoT) technology with deep learning to create a dynamic and adaptive traffic control mechanism.

IoT-enabled surveillance cameras are installed at various intersections to continuously capture live video feeds of the traffic. These video streams are transmitted to a processing unit, where image frames are extracted and analysed using the ResNet (Residual Network) model. ResNet, known for its high accuracy and efficient feature extraction capabilities, is used to detect and classify vehicles in each lane. By counting the number of vehicles, the system determines the traffic density in real time. Based on this density information, the system automatically adjusts the traffic signal duration to allow smoother vehicle movement in congested lanes, thus reducing waiting times and improving overall traffic flow.