Machine Learning-Based Real-Time Traffic Control System

Road traffic has been an open challenge in a highly-populated area in the world. India is one of them where traffic management is a crucial problem to solve. Road traffic congestion, traffic rule violation, road accidents are the outcome of limitless traffic. The paper attempts to highlight the Real-Time Traffic Control System (RTTCS) monitoring vehicles, passengers, detecting objects on the road, and license plate detection. This paper proposes an RTTCS based on Machine Learning (ML) model, which takes traffic streaming as an input, monitors the traffic by detecting objects, finds the count of the obj ects per class, detects license plates and recognizes license plate characters using Easy-OCR. For depth analysis, the RTTCS analysis is performed for a particular location at various timestamps for easy traffic monitoring. The proposed system uses the You Only Look Once version-4 (YOLOv4) for object detection (e.g., bicycle, motorbike, cars). The transfer learning concept uses pre-trained convolutional weights to optimize the model.