A Computer Vision Based Approach for Automated Traffic Management as a Smart City Solution

This study aims to provide a solution to the incessant land acquisition to surmount growing traffic by promoting the application of adaptable lane dividers meant to be implemented in smart cities. A flexible lane span manipulates the width of the road as a whole, avoiding the need for road expansion. Video data is obtained from cameras placed along a single stretch and is analyzed in real-time. The model uses Computer Vision, ROI (Region of Interest) based execution, exploiting both traffic speed and occupied lane area to determine traffic density. Each camera is assigned a priority value with cameras down the lane possessing higher priority. The decision of each camera constitutes the final decision. The design also adopts pattern recognition based on learning, besides real-time analysis for more conclusive results.