Automated traffic congestion estimation via public video feeds Route planning is essential for commuters and whilst many developed companies and urban cities have reliable systems certain areas in the world have to cope with inaccurate systems or the lack of. There is a current surge in public video feeds that are directed on key roads with some being subsidies by governments. Video footage for two arterial and two rural roads have been gathered for 6 days with a total of 154 hours of footage together with video recordings of the traffic congestion level for the same roads as estimated by an industry leading traffic application. In this research, we are presenting a solution that given such a public video feed will automatically provide traffic congestion levels after a 24 hour selfcalibration period. We have benchmarked our solution with an industry leading traffic application and have obtained a 16% higher precision and an overall accuracy of 86%. This research is based on image processing techniques and has addressed clear weather conditions during the day and recommend further research into the estimation traffic during challenging scenarios.

Measuring Of RealTime Traffic Flow Using Video From Multiple IPBased Cameras Developing an intelligent transportation system that contains the basic information of the realtime traffic flow helps Lembaga Lebuhraya Malaysia to monitor and manage the traffic flow of our country. The main purpose of this paper is to detect and count vehicles along with its direction either towards North or South in realtime traffic flow by using multiple IPbased cameras. Open Source Computer Vision Library (OpenCV) and Python Programming language is used to implement the method developed. The project also provides an improvement by developing the system to be implemented in multiple cameras for realtime traffic measurement. The number of vehicles and their directions which are the output of the developed system is stored in json file to keep track of the data as well as the name of cameras with the time it is processed. The system developed is hoped to be a starting point in developing a more complex system.