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8 Fun Computer Vision Project Ideas For Beginners [2024]

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5th Oct, 2022
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8 Fun Computer Vision Project Ideas For Beginners [2024]

AI and machine learning have multiple branches, and computer vision is among the most prominent ones. One of the best ways to learn computer vision is by completing computer vision projects. That’s why, in this article, we have shared our top computer vision project ideas. They are for various skill levels, so you can choose a project (or multiple projects) according to your expertise. Let’s get started. 

Our Top Computer Vision Project Ideas

1. Perform Face Detection on Your Family Photos

Face detection is among the most popular computer vision project ideas. It has applications in many areas, security, social media, healthcare, etc. Whether you’re a beginner or an advanced developer, you can find a face detection project to work on with ease. Face detection is a considerable section of computer vision, and if you want to become an expert, you should work on several face detection projects.

If you’re a beginner, you should start with the Face Recognition library in Python. It is an easy to use the library with straightforward commands for specific face recognition implementations. However, you should be experienced in programming in Python to use this library. This library has a simple face_recognition command, which lets you identify faces quickly. 

On the other hand, if you’re an advanced programmer, you can use OpenCV to perform face identification in images. You can combine the Face Recognition library with other Python libraries to make more complex solutions. 

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How to Make the Project More Challenging

You can develop a model that identifies faces in videos. On the other hand, you can teach your model to recognize people in group photos. This means the model should recognize the person present in the picture. For that, you’d have to train the model with a custom dataset of the people’s images you want the model to recognize. 

2. Build a People Counting Solution

People counting is a prominent application of computer vision technology. With the recent pandemic, the value of people counting solutions increased substantially. You can use OpenCV and Python to build a model for calculating the total number of people present in a picture. 

A people counting solution can be instrumental in the current world where social distancing is vital for survival. As a beginner, you should first start with a simple people measuring model that can count the number of people present in a picture. 

How to Make the Project More Challenging

If you want to make this project more complicated or advanced, you can add more functionalities. For example, create a solution that counts the number of people present in a public place to enforce social distancing rules. There would be a certain quantity of people beyond which it would be improbable to have proper social distancing followed.

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3. Practice Object Recognition with the Open Images Dataset

If you are a beginner and haven’t worked on image processing projects before, you should get familiar with Google’s Open Images Dataset. It’s a collection of around nine million different images containing rich annotations. The photos are of various types, and many even have complex scenes of multiple objects. It has object bounding boxes, visual relationships, labels annotations, and many things that make it perfect for any computer vision enthusiast.

You can train your models with the Open Images Dataset. 

Learn: Tensorflow 2.0 Image Classification

4. Perform Image Classification on CIFAR-10

Image classification is a prominent deep learning application of computer vision. To work on this project, you should be familiar with Python, Keras, and TensorFlow. You can use the CIFAR-10 dataset to perform image classification. It has more than 60,000 pictures with ten target classes.

Every target class has 6,000 images. The photos present in this dataset are of relatively low resolution, so you can test out multiple algorithms without facing technical issues. Image classification focuses on separating the pixels of a picture according to the classes they belong to. 

You’d have to build a convolutional neural network through Keras for completing this project. 

You won’t have to download the CIFAR-10 dataset separately if you have Keras already. That’s because it is present in the datasets module. 

By working on this project, you’ll learn a lot about image classification. You’d also get to use TensorFlow and Keras, two of the industry’s most prominent AI tools. Working on this project will help you explore their features and functionalities. 

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How to Make the Project More Challenging

To make this project more challenging, you can add a GUI to your solution. The GUI (graphical user interface) would allow a user to feed images into the model’s test dataset for analysis. You should be familiar with Python’s Tkinter library to build the GUI. This way, you’d have a working software solution that can perform image classification on desired datasets. 

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5. Detect Colours in Images

Detecting colors in images is a crucial part of computer vision. It might seem easy to do, but it’s certainly not. There are many applications to a color detection model. For example, you can use it in photo-editing software. Colour detection is a huge part of the green screening, which has become quite popular in the media and entertainment industry. 

The object of your model would be to detect every color present in an image. You can use the Google-512 dataset to train your model. It’s a collection of internet images separated according to their primary colors, and it has black, red, white, yellow, orange, blue, gray, purple, pink, and brown colors. You can get the Google-512 dataset here.

Checkout: Artificial Intelligence Project Ideas

How to Make the Project More Challenging

As we mentioned earlier, green screening is probably the most popular application of color detection technology. Green screens are, as the name suggests, are screens of green color. They allow users to change the background of an image or video into any desired photo or video. The software would then identify the screen’s green color and replace it with the selected image or video. You can build a model that resembles the green screen. 

6. Learn Object Tracking

Object tracking is an advanced-level application of computer vision. While we’ve mostly discussed image processing projects in our list, this project focuses on video analysis. An object tracking model would identify and track a specific object in a video. The model performs two particular tasks in tracking objects. It has to predict the object’s next state according to the current state. Then the model has to correct the state according to the object’s real condition.

You can use the TLP dataset to work on this project. It has 50 High-Def videos of real-world scenarios. It has more than 400 minutes of various clips. The creators of the TLP dataset also have a smaller dataset called TinyTLP. It has the first 20 seconds of every clip present in the TLP dataset. You can learn more about the TLP dataset here.

7. Count Vehicles in Images and Videos

A vehicle counting system can be highly useful for managing traffic. It can be helpful for driverless cars too. In this project, you’d build a vehicle counting model that can count the number of cars and bikes present in an image. It should be able to count them under challenging scenarios (images with high shadows or occlusions). To work on this project, you can use the Vehicle Image Database, a collection of more than 3425 vehicle images. 

You can train your model to recognize cars by using that dataset. You should be familiar with object recognition before you start working on this project. The database has 3900 images of road sequences without vehicles as well. There are multiple classes available in the Vehicle Image Database, so you can train your model rigorously. 

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8. Build a QR Code Scanner 

You must’ve noticed how popular QR codes have become in recent years. UPI, Paytm, PhonePe, and other digital payment apps have made QR codes widely popular because of their simple implementations. QR code scanners utilize computer vision for image analysis. So, in this project, you have to build a QR code scanner. 

You’d have to use OpenCV to build the scanner. This means you should also be familiar with programming in Python before you start working on this project. Apart from OpenCV, you will use pyzbar, in this project, a Python library dedicated to scanning QR-codes and barcodes.

Working on this project will make you familiar with the real-world applications of computer vision. Also, you would have an operational QR-code scanner that you can integrate into another project. 

How to Make the Project More Challenging

To make this project more complicated, you can add the functionality of scanning barcodes in your model. There are substantial differences between barcodes and QR-codes. You’d need to use pyzbar for creating a barcode scanner. You can take this project a step further by combining the QR-code scanner and the barcode scanner into one solution. 

Read: Machine Learning Project Ideas

Learn More About AI and Machine Learning

We hope you enjoyed reading this article on computer vision project ideas. If you have any questions or suggestions on this topic, please let us know. We’d love to hear from you.

By working on multiple computer vision projects, you can develop the necessary skills to become an expert in this field. Projects help you in finding your shortcomings as well. If you want to learn more about computer vision, you can head to our blog. 

On the other hand, if you want a personalized learning experience, we recommend taking a machine learning course. The machine learning course will allow you to learn from industry experts through interactive quizzes, videos, and projects. 

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Which image processing projects did you like the most? Are you planning on working on a project? Let us know in the comments. 


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1How is Computer Vision different from Image Processing?

Computer Vision is a higher-level version of image processing in which the input is an image and the result is an interpretation of the image rather than an image. The fundamental distinction is between the objectives rather than the techniques. Image Processing is used when the purpose is to improve an image for subsequent usage. Computer Vision, on the other hand, is concerned with simulating human-like vision for the purpose of object detection. It's critical to understand the key distinctions. Image processing is one of the components of Computer Vision, as Image Processing methods are used to make Computer Vision work properly.

2What are the applications of Computer Vision?

Self-driving cars use computer vision to understand their environment. Cameras around the car gather video from various angles and send it to object recognition software, which processes the images in real time to locate road edges, read traffic signs, and detect other cars, objects, and pedestrians. Face identification applications, which use computer vision to match images of people's faces to their identities, are another area where computer vision plays a key role. Computer vision has also played a significant role in health-tech advancements. Computer vision algorithms can aid in the automation of jobs like detecting malignant moles in skin photos and locating symptoms in x-ray and MRI scans.

3What is the role of CV in augmented and mixed reality?

Computer vision is particularly crucial in augmented and mixed reality, which allows computer devices like smartphones, tablets, and wearable technology to overlay and embed virtual objects on real-world imagery. AR gear detects items in the real environment using computer vision to identify where a virtual object should be placed on a device's display. Computer vision algorithms, for example, can assist AR applications in detecting planes such as tabletops, walls, and floors, which is a key aspect of defining depth and dimensions and placing virtual items in the real environment.

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