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Moving Vehicle Detection for Measuring Traffic Count Using OpenCV

Nilesh J. Uke1 and Ravindra C. Thool2
1.Sinhgad College of Engineering, Department of Information Technology Pune, India
2.Department of Information Technology, S.G.G.S. College of Engineering and Technology, Nanded, India
Abstract—System in this paper is designed and implemented with Visual C++ software with Intel's OpenCV video stream processing system to realize the real-time automatic vehicle detection and vehicle counting. Expressways, highways and roads are getting overcrowded due to increase in number of vehicles. Vehicle detection, tracking, classification and counting is very important for military, civilian and government applications, such as highway monitoring, traffic planning, toll collection and traffic flow. For the traffic management, vehicles detection is the critical step. Computer Vision based techniques are more suitable because these systems do not disturb traffic while installation and they are easy to modify. In this paper we present inexpensive, portable and Computer Vision based system for moving vehicle detection and counting. Image from video sequence are taken to detect moving vehicles, so that background is extracted from the images. The extracted background is used in subsequent analysis to detect and classify moving vehicles as light vehicles, heavy vehicles and motorcycle. The system is implemented using OpenCV image development kits and experimental results are demonstrated from real-time video taken from single camera. We tested this system on a laptop powered by an Intel Core Duo (1.83 GHZ) CPU and 2GB RAM. This highway traffic counting process has been developed by background subtraction, image filtering, image binary and segmentation methods are used. This system is also capable of counting moving vehicles from pre-recorded videos.

Index Terms—Computer vision, OpenCV, segmentation, video detection

Cite: Nilesh J. Uke and Ravindra C. Thool, "Moving Vehicle Detection for Measuring Traffic Count Using OpenCV," Jounal of Automation and Control Engineering, Vol. 1, No. 4, pp. 349-352, Dec., 2013. doi: 10.12720/joace.1.4.349-352
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