- Frontend: Utilizes Node.js, HTML5, CSS3, Javascript with JADE view engine and Express, to offer a user friendly frontend for login and and user interaction. The goal for the users was to have a good user experience for video upload, and also to allow them to view real time, processed video straight from their webcam.
- Backend Server: Nginx and distributed computing networked capabilities utilizing AWS and allows for real time video processing when combined with the video processing system.
- PostgreSQL Server: First normal form due to simplicity in design. It only contained a user table and a video table for each video which has a foreign key associated with username. The video table contains information on filepath of a video file and a json with all of its information.
- Video Processing: FFMPEG, OpenCV, dlib and EyeLike, combined with delaunay’s triangle algorithm breaks a video into component frames, scrapes metadata and adds 68 points with meshed lines. It also bounds the eyes and tracks the pupil locations which are then written to the database.
- Utilizing OpenCV and dlib for face recognition and point detection
- To draw and connect points using the Delaunay triangle algorithm
- Implementing a multi-server architecture
- Real time video processing
- Staying on schedule
- Choosing between quality and speed of processing
- Keeping large operations fast (such as FFMPEG un-stitching and restitching of frames)
- To repurpose multiple existing codebases into a different use
- Ensuring frequent meetings amongst busy schedules
- Yaw, Pitch and Roll detection
- Getting frames into and out of EyeLike
OpenCV Cmake FFMPEG Dlib Python 2.x Numpy PostgreSQL JQuery Video.js Moment.js Connect-multiparty express express-session Jade pg-promise
Copyright (c) [2017] [Andrew Levon Ajemian, Matthew Evan Binning, Pei Lian Liu, Long Trinh]
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