Face Tracking Pan-Tilt Camera




Introduction: Face Tracking Pan-Tilt Camera

A camera pan-and-tilt gimbal that is capable of tracking faces using computer vision and machine training so it can follow you around as you film or vlog. This way your face is always the centre of the action!

You can also train this gimbal to follow not only human faces but also objects, pets, food, or anything you can imagine by using different training data.

(Sorry for the potato quality of the MacBook camera)

Step 1: Support Me on Patreon

Consider supporting me in Patreon. I'd like to make better quality Instructables and more complex and interesting projects but most of these projects are quite costly and time consuming.


Thank you for your support.

Step 2: Materials

(2) Servos

(1) Arduino Uno

(1) Jumper cables

3D print one

or buy one http://www.ebay.ca/itm/Pan-Tilt-Camera-Servo-Gimb... (includes the servos)

or make one of your own using scrap wood pieces and glue

Step 3: Installing Python

If you have programmed in Python (2.7) before, you can skip this step.

The language I used for the programs that track the faces and send the coordinates to the Arduino is Python so it must be installed in your computer to be able to run the code.

I personally use PyCharm (download here: https://www.jetbrains.com/pycharm/) as my main python IDE but you can use other IDE's like WingIDE, or just use a text editor and terminal. Most of the time, I just use Sublime Text and Terminal (built-in command line tool for a Mac). But, if you've never programmed or used Python before, I recommend starting with a simple free IDE like WingIDE (download here: https://wingware.com).

My code uses Python 2.7 so download that from here: https://www.python.org/downloads/release/python-2...

You can also use homebrew (this only applies to Mac users) to install python by typing the following line to terminal:

brew install python

Step 4: Installing OpenCV and NumPy

If you already have OpenCV and NumPy installed in your computer you can skip this step.

The libraries I am using are OpenCV and NumPy so both libraries must be installed in your computer. OpenCV is an open-sourced computer vision library used to find faces in still images. I've adapted my code to work on frames of a live video. While, NumPy is a powerful numeric calculations library for Python.

For Windows users install OpenCV by following this guide: http://docs.opencv.org/3.2.0/d5/de5/tutorial_py_s...

For Mac users I often had trouble installing it the way OpenCV recommended so I use homebrew to install it. Follow this set of instructions on how to install homebrew and OpenCV: https://gist.github.com/stilist/cd7fdfabbf9ec5ed8...

Installing OpenCV can be a hit-or-miss sometimes, so test if you have OpenCV using the snippet of code below.

import cv2

If you do not get any error messages when you run it, then you have installed it successfully

To install NumPy, open your command line tool (Terminal for Mac/Linux users and Command Prompt for Windows users) and type the following

pip install numpy

Pip is a package that comes when you install Python in your computer so it works cross-platform.

Similarly, if you want to check if NumPy has been installed successfully, run the following snippet of code below:

import numpy

Step 5: Install ArduinoIDE

If you already have ArduinoIDE installed in your computer you can skip this step.

You can download the ArduinoIDE for the Arduino website: https://www.arduino.cc/en/Main/Software

Setup your your Arduino by connecting it to your computer and selecting the correct board and com port.

Step 6: Wiring the Servos

The wiring for this project is pretty straight forward. Follow the diagram above.

Step 7: Printing/Assembling the Camera Gimbal

You can 3d print this camera gimbal designed by Jake King:


Make sure that before you place the servos onto the camera gimbal that you centre it first. You can centre a servo by applying +5V VCC to the red pin and GND to the black pin. The servo will automatically rotate to the centre position.

Step 8: Run the Code

You can download the code from my Github. I will update this as I continue to improve the code.


First run the main.py using your Python IDE or if you are using terminal like I am, type python main.py. This will launch the video capture mode of your camera.

Then, compile and upload the Arduino sketch.

To exit the video capture mode, go to the screen that is running the video capture -- Python IDLE -- and hit "q" to quit.

Step 9: Calibrating the Camera

Once you run the code, you will get two prompts. One is the average distance of the person the track from the camera and the other is the field of view of the camera.

Step 10: More...

Change the training data, so if you want to film your cat for example, copy and paste the pictures of your cat to the training folder and the camera will be tracking your cat. You can learn more about training the HaarCascade here: http://memememememememe.me/post/training-haar-cas...

Run the code in a Raspberry Pi, which is a pretty much the same steps since I use a Mac and Raspberry Pis are computers that run on Linux. Now, you'll have a more compact and portable camera setup.

If you want to improve the system you can also try playing with the code and controlling Depth and z-axis zoom functionality.

Step 11: Vote for Me!

I've entered this instructables to both the robotics and the photography contest. Please vote for me if you liked it.

Photography Contest 2017

Participated in the
Photography Contest 2017

Robotics Contest 2017

Participated in the
Robotics Contest 2017

1 Person Made This Project!


  • 3D Printed Student Design Challenge

    3D Printed Student Design Challenge
  • Edible Art Challenge

    Edible Art Challenge
  • Unusual Uses Contest

    Unusual Uses Contest



Question 9 months ago on Step 11

Hello! Thank you so much for your great documentation! (It's hard to find nowadays!)
I may be having a bit of a brain fart, but where (physically) do you connect your camera? Im using a HuskyLens which has the 4-color sensor cable (Rx, Tx, -, +). I can figure out where the RxTx cables go.... and the ground. Where did you connect your camera? was it the 3.3V or 5v? Or Vin? Hope to hear from you soon!


4 years ago

Hello, I'm following this tutorial and have found some hiccups.

First off, I fixed a thing in the code because python was giving errors, at the following place:

vid2photos.py - line 7, changed cv2.cv.CV_CAP_PROP_FPS to 5, which I saw in the docs of the newer OpenCV library. The new code therefore is cap.set(5, 1)

Now, if I run python main.py, I get the camera view screen where my camera video is shown. This all seems to work correctly. I also put the Arduino code on the Arduino, which also uploads.

Unfortunately, this is all that happens. I don't see like a square marking my face on the little view and my arduino doesn't do much either. I downloaded the current version of the github repo and am using a Windows PC.

Do you know what I can do about this? Are there steps I missed? Right now I'm at step 8, running python pixel2degree.py returns a few errors (the error at line 44 where == is missing instead of = I already fixed, the current error is that int doesn't have a a get index function, guessing because of (parseList[i])[i]), and before I move on to try and fix that I would like to know what might be going on.

Thanks in advance!


4 years ago

And i must try it ?


Reply 4 years ago

Thank you! Awesome profile pic btw.


4 years ago

Nice I like it, I haven't used openCV before cause I wanted to track humans in general but now I realise that it's actually a neural network haha so I'll put it on the never ending projects to do list. Voted!


Reply 4 years ago

Thanks! If you're interested in training datasets you can also consider Clarifai. It's a really simple machine learning computer vision API, but it doesn't have localization yet.