@article{MAKHILLJEAS201712814384,
    title = {Realtime Stereo Vision for Vehicle Detection,
Classification and Counting Using Raspberry Pi},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {8},
    pages = {2207-2213},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.2207.2213},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.2207.2213},
    author = {Mohammad,Rudi,Hadyan and},
    keywords = {Vehicle detection,classification and counting,intelligent transportation system,openCV,raspberry Pi},
    abstract = {In Indonesian toll road system, still found the lack of information on the number of vehicles passing
through to the road in realtime. This is caused by the absence of detection and vehicle counting system that
work in realtime applied on the road toll and this situation can cause difficulties to controll the traffic on the toll
road. Therefore, it necessary to study an automated system that works in realtime doing precisely identifying
the type of vehicle and calculate it. In this research, we built a prototypes of visual based vehicle detection,
classification and counting, made using mini PC raspberry Pi as the central processing and USB camera modules
as input devices and arrange in Stereo System to reduce the inability to detect vehicles behind another vehicle.
Some algorithms of computer vision assembled from the functions that exist in the library openCV. For realtime
segmentation method using Haar-like features, then we uses that found features as reference from every stereo
images and apply the ratio test to find the best matches and extract the locations of matched keypoints in both
the images. RANSAC algorithm is used to minimize errors that occur after matching. So, best matches which
provide correct estimation (inliers) and throw out remaining outliers. The results showed improvements of
vehicles that can be detected and counted.}
    }