We will look at 3 different ways to identify objects and faces and how they are similar and different. 

• Object Recognition 

• Face Recognition 

• Face Tracking 

Robotics are applied to each of the computer vision models through the use of a servo being triggered by some event. 

We will also detail some of the limitations of each model with respect to the distance from the camera.  This can provide some insight into the effectiveness of each model when applied to common scenarios including security cameras, automobile self driving and robotic applications. 

Initial setup 

We initially researched Youtube videos on the Raspberry Pi and Vision 

We ordered some equipment and had to learn how the Raspberry Pi worked and the camera. 

There were struggles and learnings throughout this project

 

Hardware and Key Technologies 

• Raspberry Pi 4 8GB 

• Raspberry official Camera 

• Pimoroni Pantilt Camera 

• SG90 9g micro servo 

• OpenCV 

• Python language 

 

Getting each Individual Model working 

There were a number of videos that we researched on YouTube.com to get each of the 3 models working.  

All code was placed in a central folder with an individual folder for each of the 3 computer vision folders. 

Some modifications were made to some of the computer vision models to incorporate a trigger to operate the servo for a given event. 

Below is a summary of each computer vision model and its location on our Raspberry Pi 

 

 

Each of the following demonstrations can be found in a subfolder on the Raspberry Pi in the /home/pi/demo folder. Use File Manager 

Pimoroni Pantilt Camera Setup 

Folder: /home/pi/demo/keyboard 

Open a terminal and type: 

cd /home/pi/demo/keyboard 

python smooth.py 

smooth.py  - Simply shows the control of the camera with a python program 

python KeyboardPanTilt.py 

KeyboardPanTilt.py - allows user to control the keyboard by the keyboard and use p to take pictures and q to quit 

Face Tracking 

Folder: /home/pi/demo/facetracking 

Open the following File Manager and go to the folder above 

Run the programs below in Thonny 

facetracker.py - Tracks a face on the screen with a green square 

sentry.py - Tracks but with sentry mode, will present a blue square 

Object Detection 

Folder: /home/pi/demo/objectdetection 

Open the following File Manager and go to the folder above 

Run the programs below in Thonny  

object-ident.py - identify individual objects in the camera 

Servo 90g setup: Red to pin 4, Brown to pin 14, Orange to pin 16 

 

servo.py - demonstrate how a servo can be manipulated 

servoangle.py - experiment further with the servo (Setting angles between 0 and 180 degrees) 

Object-ident-filter.py - move the servo on the capture of a certain object 

Face Recognition 

Folder: /home/pi/demo/facerecognition 

Open the following File Manager and go to the folder above 

Run the programs below in Thonny  

head_shots.py - capture some pictures of a face for training 

Create a folder in the dataset folder that matches the name=’Aidan’ in  this program 

Run the program and press spacebar to capture 6-10 photos 

train_model.py - train the model on the pictures in the data set folder 

Run this program to train the model 

facial_req.py - run the trained model to identify certain facial images with the camera 

Run this program to see it identify faces 

facial_req_withservo.py - run the trained model to identify certain facial images with the camera 

Update the trackingName in this program to move the servo for a specific person 

Run this program to identify faces and turn the servo when it finds the trackingName person 

Servo 90g setup: Red to pin 4, Brown to pin 14, Orange to pin 16 

 

 

Research 

Once we got each of the computer vision models we did some basic experimentation with each of them and found the following for each model: 

 

Object Recognition 

Object Recognition worked on a number of predefined objects once the program was run.  We experimented on a number of objects. The code could be modified to trigger a servo (robotic extenstion to the system) when looking for a specific object such as a  bottle, teddy bear or a person. 

It was noted that there sometimes was a lag in object recognition due to a build up of frames for the program to process. A quicker processor might solve this. 

Face Recognition 

The goal of face recognition is to identify different faces from different people. 

There are 3 phases to face recognition: 

• Data Collection: gathering images/photos of a person under their folder 

• Training: training the program to match which facial features to each person 

• Running the model: running the model to detect faces 

It was noted that it might have trouble if the person tilted their head in a way that was different from the training data. 

There was a lag due to processing sometimes where the program was several seconds behind. 

A servo could be triggered when a certain individual’s face appeared. 

Face Tracking 

This involved setting up the Pimoroni PanTilt Camera and running it’s program. It essentially locks onto a face and can move to track the face around the room. 

There was a sentry mode used to search out faces in the room as a mobile security camera might do. 

The camera itself has 2 servos for vertical tilting and horizontal panning. 

Once we got each of the computer vision models we did some basic experimentation with each of them and found the following for each model: 

Object Recognition 

Object Recognition worked on a number of predefined objects once the program was run.  We experimented on a number of objects. The code could be modified to trigger a servo (robotic extenstion to the system) when looking for a specific object such as a  bottle, teddy bear or a person. 

It was noted that there sometimes was a lag in object recognition due to a build up of frames for the program to process. A quicker processor might solve this. 

Face Recognition 

The goal of face recognition is to identify different faces from different people. 

There are 3 phases to face recognition: 

• Data Collection: gathering images/photos of a person under their folder 

• Training: training the program to match which facial features to each person 

• Running the model: running the model to detect faces 

It was noted that it might have trouble if the person tilted their head in a way that was different from the training data. 

There was a lag due to processing sometimes where the program was several seconds behind. 

A servo could be triggered when a certain individual’s face appeared. 

Face Tracking 

This involved setting up the Pimoroni PanTilt Camera and running it’s program. It essentially locks onto a face and can move to track the face around the room. 

There was a sentry mode used to search out faces in the room as a mobile security camera might do. 

The camera itself has 2 servos for vertical tilting and horizontal panning. 

Data 

In addition to learning more about how each computer vision model worked we explored the general maximum effective range of each of the models. 

We measured the maximum distance that each model could function from the camera.  It must be noted that conditions can be varied. 

This test was carried out by averaging the 10 trials from both of us  

 

Computer Vision Model	Facial Recognition	Face Tracking	Object Identification
Maximum Range	2.29 m (7.5 feet)	2.6 m (8.5 feet)	6.1 m (20 feet)

 

We acknowledge that the data could change depending on a number of factors including lighting, background noise. We believe that the specific distance is not that important but understanding which model can work further from the camera and which are most limited. 

We demonstrated that we could get 3 different computer vision models working for the first time.  This was challenging as we had to navigate getting the Raspberry Pi up and running. We had to spend a lot of time getting each of the models to work and understanding them. There were additional challenges with incorporating servo functionality as well. 

We were able to demonstrate how these computer vision models work with servos. The servos represent a robotic application.   We were able to determine the effective ranges of each of the computer vision models.   To summarize, we found that the 3 computer vision models had different effective ranges which are important when building computer vision solutions.  

The Facial Recognition model's effective range was found to be 2.29 m (7.5 feet). Face Tracking was found to have a range of  2.6 m (8.5 feet) and Object Identification had the longest range of 6.1 m (20 feet). The effective ranges are inversely proportional to the amount of detail required.   Facial Recognition requires the most detail, followed by Face Tracking which involves identification of a face but not a specific face.  Object Identification requires a lower amount of detail which means its effective range is longest.  This information is important when considering the building of a Computer Vision model and ensuring its effectiveness. 

Although this was a first attempt it was successful in understanding the potential and limitations.  

For future experiments, we can look at more complicated examples with different computer vision models or incorporating more complex robotic examples. We illustrated the use of a simple servo but a more complicated setup of multiple servos can lead to more sophisticated robotics applications. Potential applications include security cameras, automobile self driving and robotic applications. 

Core Electronics (2021a) Easiest Pan-Tilt System for the Raspberry Pi – Quick Start Guide with Pimoroni Picade HAT . Retrieved on October 5, 2024 from https://www.youtube.com/watch?v=Dc9AEFw0hww 

Core Electronics (2021b) Face & Movement Tracking System Using a Raspberry Pi + OpenCV + Pan-Tilt HAT + Python . Retrieved on October 5, 2024 from https://www.youtube.com/watch?v=T_892SKVNf4 

Core Electronics (2021c) Object Identification & Animal Recognition With Raspberry Pi + Open CV + Python . Retrieved on October 5, 2024 from https://www.youtube.com/watch?v=iOTWZI4RHA8 

Core Electronics (2021d) Face Recognition With Raspberry Pi + OpenCV + Python . Retrieved on October 5, 2024 from https://www.youtube.com/watch?v=o-x1PE0LVKM 

ExplainingComputers(2020). Raspberry Pi Servo Motor Control. Retrieved on January 21, 2025 from https://www.youtube.com/watch?v=xHDT4CwjUQE 

 Mafukidze, Harry (ND). How to use Servos on the Raspberry Pi. Retrieved on January 20, 2025 from https://www.circuitbasics.com/how-to-use-servos-on-the-raspberry-pi/  

Miller, Liz. (2024). How to Control a Servo with Raspberry Pi. Retrieved January 20, 2025 from https://www.learnrobotics.org/blog/raspberry-pi-servo-motor/  

Ogonyesolomonoche (ND). Controlling Servo Motor (Sg90) With Raspberry Pi 4. Retrieved on January 20, 2025 from https://www.instructables.com/Controlling-Servo-Motor-Sg90-With-Raspberry-Pi-4/  

We worked a lot to get these computer vision models working.  We are thankful to Core Electronics to publish videos showing how to do much of these. Also we are thankful for the Open CV and COCO teams for publishing open source computer vision code that works.