Car speed and lane and distance detection by using Opencv

YEAR : 2023


Moving car information in traffic video signals contains plenty of important information on road safety. In this paper, we propose an algorithm that extracts and tracks feature points of cars. The information is indispensable for measuring moving car speed, counting the number of cars, monitoring distance between two cars, classifying running directions, and monitoring congestion situation. Utilizing Harris operator for detecting edges and corners, feature points are extracted, followed by block-matching to track the feature points in successive video frames. Many cars can be tracked at the same time automatically since the information is obtained from video sequences. As an example, this paper shows how car speed can be measured Average speed of the vehicle is 100 km per hour. Image processing methods are a combination of methods of colour region, line selection, canny edge detection, and Hough transform. The result shows this algorithm needed to be add some method that can change the parameters during day and night adaptively. Overall the implementation method in Python Language can successfully detect the road lane with accuracy above 90 percent.



Hard Disk : 500GB and above
RAM : Minimum 4GB
Processer : I5 and above


Operating System : Windows 7,8,10 (64 bit)
Software : Python
Tools : Anaconda (Jupyter notebook IDE)


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