@article{MAKHILLIJSSCEA202013228777,
    title = {Traffic Detection Using OpenCV},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {13},
    number = {2},
    pages = {18-22},
    year = {2020},
    issn = {1997-5422},
    doi = {ijssceapp.2020.18.22},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2020.18.22},
    author = {Teena,Mandar,Siddharth and},
    keywords = {SVM,neural networks,OpenCV,image processing,Machine learning,tensorflow},
    abstract = {Traffic jams have become one of the biggest
problems any metropolitan city faces in today’s time. This
paper suggests implementing a smart traffic detector using
OpenCV. The density of vehicles on the road keeps
increasing to a higher amount these days. In traffic signal,
people waste much time particularly during the peak
hours of the day. In order to solve this problem of high
traffic pressure, it is indispensable to solve traffic
congestion. The frustration that is faced by people during
traffic jams could also lead to mishaps such as accidents.
Thus an idea of monitoring the traffic congestion using
real-time image processing techniques and via. Central
Neural Networks, through this software has been
proposed. The theme is to determine the traffic density on
each side of the road by calculating the number of
vehicles at the traffic signal zone. In this, an input image
of traffic surveillance is shown to our trained machine
which declares whether there is traffic or not by
judging via. the number of vehicles seen. After the
image acquisition, the image undergoes various image
pre-processing, image enhancement and edge detection
techniques. This project has been customized to be used
in the future to control the traffic signals as well as
monitor violators and avoid inconvenience and accidents
as much as possible.}
    }