https://github.com/zackakil/robo-videographer
Project for a automatic videographer system for recording sports matches.
https://github.com/zackakil/robo-videographer
camera machine-vision neural-network raspberry-pi servo-controller sklearn
Last synced: 6 months ago
JSON representation
Project for a automatic videographer system for recording sports matches.
- Host: GitHub
- URL: https://github.com/zackakil/robo-videographer
- Owner: ZackAkil
- License: gpl-3.0
- Created: 2016-11-14T21:49:34.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2019-08-19T14:24:23.000Z (about 6 years ago)
- Last Synced: 2025-05-04T01:54:09.884Z (6 months ago)
- Topics: camera, machine-vision, neural-network, raspberry-pi, servo-controller, sklearn
- Language: Jupyter Notebook
- Size: 91.3 MB
- Stars: 13
- Watchers: 2
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Robo Videographer
Project for a automatic videographer system for recording sports matches.
## Technical objective
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.
## Implimentation goal
The final aim is to use the system to record a tag rugby matches with no human intervention.
## Technology to use
* Raspberry Pi
* Raspberry Pi Camera module
* OpenCV | SimpleCV
* Video camera
* Servo
## Intial Design
1. Get OpenCV/SimpleCV working on the Raspberry Pi with the camera.
2. Build a program that tracks the pixel activity.
3. Output activity value to a servo position.
## Progress
### Software
- [x] Get OpenCV/SimpleCV working on computer
- [x] Develope simple "action tracking"
- [x] Develope simple servo control on raspberry pi
- [x] Add "smooth action tracking"
- [x] Get OpenCV/SimpleCV working on raspberry pi
- [x] Merge action tracking with servo control
- [x] Optimise action finding code to run faster
- [x] Add concurrency between servo control and action detection using [python processes](https://docs.python.org/2/library/multiprocessing.html)
### Hardware
- [x] Source tri-pod
- [x] Mount servo to tri-pod
- [x] Mount camera to tri-pod
- [x] Mount Pi to tri-pod
- [x] Mount Power to tri-pod
- [x] 3D model case
- [x] wire up power splitter
### Extra Software
- [x] Load via new sd card
- [x] 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
### Other processes
- [x] Collect training data
- [x] Build tool to help label training data
- [x] Label training data
### Notes
Use `sudo modprobe bcm2835-v4l2` to activate camera.
### Resources
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
### Testing
- [ ] Compare feature engineering with prediction speed and accuracy
- [ ] Compare temporal framing sizes with prediction speed and accuracy