Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities can be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Smart Traffic Management System using Deep Learning for Smart City Applications


    Contributors:


    Publication date :

    2019-01-01


    Size :

    590878 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Management for Smart City Using Deep Learning

    Gupta, Puja / Singh, Upendra | Wiley | 2022


    Smart traffic management system for smart city

    YE QINFA / TANG QING / ZHU WANRONG | European Patent Office | 2023

    Free access

    Smart city traffic signal management system

    ZHANG FENG | European Patent Office | 2023

    Free access

    SMART CITY TRAFFIC INFORMATION MANAGEMENT SYSTEM

    KIM MYUNG SUP | European Patent Office | 2023

    Free access

    Smart City Traffic Control System

    Adwani, Kakan / Rakesh, N. | Springer Verlag | 2019