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Milestones

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  • Create front-end control panel

    Design and build a front-end control web application from which the user will be able to see a map of all the cameras, live feed from all of them and to control their light state manually or set it to automatic with the use of machine learning.

    Overdue by 6 year(s)
    •
    Due by May 1, 2020
    •3/6 issues closed
    50% complete3 open3 closed

  • Stylizing, documentation & misc

    Stylize the repository and write the required project documentation.

    Overdue by 6 year(s)
    •
    Due by June 1, 2020
    •1/1 issues closed
    100% complete0 open1 closed

  • Deploy central server

    By this point, the classification camera algorithm and the embedded exist as stand-alone components. Figure out the communication between them to finally connect everything and reach a working state of the project. Create the server API for handling the request from the Control panel and the Raspberry Pi. Deploy the classification model onto the server.

    Overdue by 6 year(s)
    •
    Due by May 1, 2020
    •4/10 issues closed
    40% complete6 open4 closed

  • Program the embedded

    Figure out the I/O of the embedded controller, as well as how it is handled by the embedded it self (for ex.: lights should not instantly switch from green to red, but first need to go through the mid-ground of yellow).

    Overdue by 6 year(s)
    •
    Due by April 30, 2020
    •13/13 issues closed
    100% complete0 open13 closed

  • Complete the classification model

    Project a classification machine learning model, which will take camera data and return data about the vehicles present.

    Overdue by 6 year(s)
    •
    Due by March 31, 2020
    •8/15 issues closed
    53% complete7 open8 closed

  • Construct the embedded project

    Collect all the parts listed in the schematics and construct the device in real life.

    Overdue by 6 year(s)
    •
    Due by May 17, 2020
    •2/4 issues closed
    50% complete2 open2 closed