Skip to content

SFU Capstone Project (ENSC 405/440); High-precision deep learning object tracking system

License

Notifications You must be signed in to change notification settings

YodaEmbedding/eagle-eye-tracker

Repository files navigation

License: MIT Build Status

Eagle Eye Tracker

SFU Capstone Project (ENSC 405/440).

Group members: Mateen Ulhaq, Bud Yarrow, Arman Athwal, Naim Tejani, Victor Yun, Martin Leung

GUI

Simulation video

This project features a general-purpose object tracking system. Typical applications include:

  • Sports tracking (ball, puck, racing car)
  • Photography (moving car shots, moon)
  • Film industry (automated tracking of subject with adjustable parameters, e.g. rule of thirds, smoothness)
  • Drone alert system

This project makes use of the following custom built physical system with 2 degrees of freedom (azimuth, elevation):

Solidworks model

Installation

Install the following:

  • Anaconda 3 (with matplotlib, opencv, numpy)
  • Node Package Manager (npm)
  • Electron

Set your PATH environment variable to include the Anaconda 3 directory. It is recommended that you put this path at the beginning of PATH.

To install additional Python libraries used by this repository:

python3 setup.py install

To install Electron:

cd gui
npm install --save-dev electron

Also, clone:

For NXT installation instructions, see this version of README.md.

Usage

We provide the following Make targets:

make run_sim    # Run simulation on PC
make run_gui    # Run GUI on PC
make run_rpi    # Run on Raspberry Pi (ensure repo cloned onto rpi)
make test       # Run tests
make doc        # Build documentation
make doc_run    # Build and view documentation

Formal Documents

About

SFU Capstone Project (ENSC 405/440); High-precision deep learning object tracking system

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published