Project for a automatic videographer system for recording sports matches.
The aim is to create a computer vision tracking system that can run on a Raspberry Pi using the camera module that tracks the action happening on a rugby pitch.
The final aim is to use the system to record a tag rugby matches with no human intervention.
- Raspberry Pi
- Raspberry Pi Camera module
- OpenCV | SimpleCV
- Video camera
- Servo
- Get OpenCV/SimpleCV working on the Raspberry Pi with the camera.
- Build a program that tracks the pixel activity.
- Output activity value to a servo position.
- Get OpenCV/SimpleCV working on computer
- Develope simple "action tracking"
- Develope simple servo control on raspberry pi
- Add "smooth action tracking"
- Get OpenCV/SimpleCV working on raspberry pi
- Merge action tracking with servo control
- Optimise action finding code to run faster
- Add concurrency between servo control and action detection using python processes
- Source tri-pod
- Mount servo to tri-pod
- Mount camera to tri-pod
- Mount Pi to tri-pod
- Mount Power to tri-pod
- 3D model case
- wire up power splitter
- Load via new sd card
- Add script auto-run with switch
- Get Pi to act as wifi base
- Connect android to Pi by wifi
- View image from camera on android via wifi
- Collect training data
- Build tool to help label training data
- Label training data
Use sudo modprobe bcm2835-v4l2 to activate camera.
http://www.raspberrypi-spy.co.uk/2015/02/how-to-autorun-a-python-script-on-raspberry-pi-boot/
https://gpiozero.readthedocs.io/en/stable/recipes.html
https://en.wikipedia.org/wiki/Optical_flow
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
https://www.modmypi.com/blog/how-do-i-power-my-raspberry-pi
https://en.wikipedia.org/wiki/Recurrent_neural_network
- Compare feature engineering with prediction speed and accuracy
- Compare temporal framing sizes with prediction speed and accuracy