Vehicles detection and counting based on internet of things technology and video processing techniques

Marwa A. Marzouk, Amr Abd El Azeem


Recent studies have proven that vehicle tracking and detection play an important role in traffic density monitoring. Traffic overcrowding can be effectively controlled if the number of vehicles expected to pass through a congested intersection can be predicted ahead of time. To overcome such impact of traffic congestion the proposed system presents a framework, using motion detection algorithms and “ThingSpeak” internet of things (IoT) platform which is used in to calculate traffic density, the proposed system capturing video with wireless internet protocol (IP) cameras and broadcasting it to the server where motion detection algorithms as background subtraction are used to obtain a quick overview of traffic density, To save cost and improve the solution, the suggested system utilizes image processing techniques as well as the IoT analytic platform “ThingSpeak” to monitor traffic density. Finally, the suggested method is used to manage traffic flow and avoid traffic crowded. The results of the studies show that the integration of IoT-based technologies with a modified background subtraction technique is effective. This method might be enhanced further to detect vehicles that break traffic laws. We may also improve this system by detecting the presence of emergency vehicles (including an ambulance or fire truck) and granting priority to those cars.


image processing; internet of things; motion detection; traffic density;

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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