Computer Vision

In this blog post, we will explore the technology behind self-driving toy cars and how computer vision can be used to enable them to navigate their environment. We will discuss the various computer vision techniques that can be implemented, including thresholding, edge detection, blob detection, optical flow, and machine learning.

Self-driving cars have been a hot topic in the technological world for quite some time now. But did you know that you can also create a self-driving toy car using computer vision? Self-driving cars are no longer just a thing of science fiction, they are rapidly becoming a reality.

The advancements in technology and computer vision have made it possible to create autonomous vehicles that can navigate their environment without human intervention. One of the most exciting applications of this technology is the ability to create self-driving toy cars using computer vision. 

We will also explore the hardware and software required to build a self-driving toy car and the challenges that need to be overcome to make it a reality. 

Requirements 

As this is a theoretical blog post, we will only discuss the necessary requirements and the overall process of building a self-driving toy car. To begin building our self-driving toy car, we will first need to gather the necessary hardware. The main components we will need are a Raspberry Pi, a camera module, a small toy car, and a few electronic components such as a motor driver and some wires.

The Raspberry Pi is a small computer that can be used to run various software and control hardware. It is perfect for our project because it is powerful enough to run computer vision algorithms and small enough to fit inside our toy car. The camera module is what will allow the car to “see” its surroundings and make decisions based on that information. 

Once we have all the hardware, we will need to set it up and install the necessary software. The Raspberry Pi runs on a Linux operating system, so we will need to install an image of the operating system on a microSD card and then insert it into the Raspberry Pi. Next, we will need to install the necessary software libraries for computer vision, such as OpenCV, on the Raspberry Pi. This will allow us to use the camera module and process the images it captures. 

Read more about computer vision with these top 7 books

Diving deeper 

Now we can start diving deeper into various computer vision techniques. This is where the fun begins! We will learn about image processing techniques such as thresholding and edge detection to identify the path that the car should follow. 

One of the key challenges in building a self-driving toy car is calibrating the camera module so that it can accurately detect the path that the car should follow. This can involve adjusting the camera’s focus, exposure, and other settings to optimize the image quality for the specific lighting and background conditions of the environment where the car will be operating. 

Another challenge is to accurately interpret the images captured by the camera and identify the path that the car should follow. This can involve using various image processing techniques to isolate the path from the background and then using that information to control the car’s motors. 

Once the car can accurately detect and follow a path, it can be further enhanced by adding additional functionality such as obstacle detection and avoidance. This can be done by using additional sensors such as ultrasonic sensors. 

Computer vision techniques 

Computer vision techniques are a set of algorithms and methods used to interpret and understand the images captured by a camera. These techniques can be used in a toy car to help it detect and follow a path, as well as to detect and avoid obstacles. 

Some of the most used computer vision techniques that can be implemented in a toy car include: 

1. Thresholding:

Thresholding is the process of converting an image into a binary image, where all pixels are either black or white. This can be done by applying a threshold value to each pixel in the image. Pixels with a value greater than the threshold are set to white, while pixels with a value less than the threshold are set to black. This can be useful for isolating the path from the background, as it allows the algorithm to easily identify the edges of the path. 

2. Edge detection:

Edge detection is the process of identifying and highlighting the edges of an object in an image. It is usually done by convolving the image with a kernel that detects edges, such as the Sobel operator, Prewitt operator or Canny operator. Each operator will have a different way of detecting the edges, and the best one to use depends on the image. 

3. Blob detection:

Blob detection is the process of identifying and tracking specific objects or regions in an image. This can be done using various techniques, such as connected component analysis, or by training a machine learning model to recognize specific objects. This is a useful technique for detecting and tracking the position of the car, as well as for detecting and avoiding obstacles. 

4. Optical flow:

Optical flow is the process of tracking the motion of objects in an image. It is typically done by analyzing the movement of pixels between consecutive frames in a video. This can be used to determine the direction and speed of the car, as well as to detect and avoid obstacles. 

5. Machine learning:

In addition to these traditional computer vision techniques, machine learning can also be used to train a model to recognize and identify objects and features in an image. This can be useful for detecting and avoiding obstacles, as well as for more advanced tasks such as object tracking and lane keeping. 

These are some of the basic computer vision techniques that can be implemented in a toy car to enable it to detect and follow a path, and to detect and avoid obstacles. There are other techniques, but these are considered the basics to get started. 

Learn in detail about Artificial Intelligence and Computer Vision for road safety

Are you ready to start your own computer vision project?

In conclusion, building a self-driving toy car using computer vision is a challenging but rewarding project that can be a great way to learn about autonomous vehicle technology and computer vision. By using techniques such as thresholding, edge detection, blob detection, optical flow and machine learning, you can create a car that can navigate its environment and avoid obstacles.

However, it is important to keep in mind that this is not a simple task and requires a good understanding of programming, computer vision and robotics. 

We hope that this blog post has provided you with the information and inspiration you need to start your own self-driving toy car project. Keep experimenting and have fun! 

In this blog, we will discuss how Artificial Intelligence and computer vision are contributing to improving road safety for people. 

Each year, about 1.35 million people are killed in crashes on the world’s roads, and as many as 50 million others are seriously injured, according to the World Health Organization. With the increase in population and access to motor vehicles over the years, rising traffic and its harsh effects on the streets can be vividly observed with the growing number of fatalities.

We call this suffering traffic “accidents” — but, in reality, they can be prevented. Governments all over the world are resolving to reduce them with the help of artificial intelligence and computer vision.  

Humans make mistakes, as it is in their nature to do so, but when small mistakes can lead to huge losses in the form of traffic accidents, necessary changes are to be made in the design of the system.

A technology deep-dive into this problem will show how a lack of technological innovations has failed to lower this trend over the past 20 years. However, with the adoption of the ‘Vision Zero’ program by governments worldwide, we may finally see a shift in this unfortunate trend.  

 Role of Artificial Intelligence for improving road traffic

AI can improve road traffic by reducing human error, speeding up the process of detection and response to accidents, as well as improving safety. With the advancement of computer vision, the quality of data and predictions made with video analytics has increased ten-folds.  

Artificial Intelligence is already leveraging the power of vision analytics in scenarios like identifying mobile phone usage by the driver on highways and recognize human errors much faster. But what lies ahead to be used in our everyday life? Will progress be fast enough to tackle the complexities self-driving cars bring with them? 

In recent studies, it’s been inferred through data that subtle distractions on a busy road are correlated to the traffic accidents there. Experts believe that in order to minimize the risk of an accident, the system must be planned with the help of architects, engineers, transport authorities, city planners and AI.  

With the help of AI, it becomes easier to identify the problems at hand, however they will not solve them on their own. Designing the streets in a way that can eliminate certain factors of accidents could be the essential step to overcome the situation at hand.  

AI also has a potential to help increase efficiency during peak hours by optimizing traffic flow. Road traffic management has undergone a fundamental shift because of the quick development of artificial intelligence (AI). With increasing accuracy, AI is now able to predict and manage the movement of people, vehicles, and goods at various locations along the transportation network.  

As we make advancements into the field, simple AI programs along with machine learning and data science, are enabling better service for citizens than ever before while also reducing accidents by streamlining traffic at intersections and enhancing safety during times when roads are closed due to construction or other events.  

Deep learning impact on improved infrastructure for road safety

Deep learning system’s capacity for processing, analyzing, and making quick decisions from enormous amounts of data has also facilitated the development of efficient mass transit systems like ride-sharing services. With the advent of cloud-edge devices, the process of gathering and analyzing data has become much more efficient.

Increase in the number of different sources of data collection has led to an increase of not only quality but quantity of variety of data as well. These systems leverage the data from real-time edge devices and can tackle them effectively by retrofitting existing camera infrastructure for road safety. 

 Join our upcoming webinar

In our upcoming webinar on 29th November, we will summarize the challenges in the industry and how AI plays its part in making a safe environment by solutions catering to avoiding human errors.  

References: 

1. https://www.nytimes.com/2022/04/19/technology/ai-road-car-safety.html 

1. https://www.clickworker.com/customer-blog/artificial-intelligence-road-traffic/ 

In this blog, we have gathered the top 7 computer vision books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these seven computer vision books. Explore a range of topics, from Computer vision to Python. 

1. Learning openCV 4 computer vision with Python 3 book by Joe Minichino and Joseph Howse: 

This book will teach you how to create a computer vision system using Python. You will learn how to use the OpenCV library, which is a cross-platform library that has been used in many research and commercial projects. Joe and Joseph in this book introduces computer vision and OpenCV with Python programming language. 

Both novices and seasoned pros alike will find something of use in this book’s extensive coverage of the subject of CV. It explains how to use Open CV 4 and Python 3 across several platforms to execute tasks like image processing and video analysis and comprehension. Machine learning algorithms and their many uses will be covered in this course. With these ideas in hand, you may design your image and video object detectors!  ~ Adam Crossling, Marketing manager at Zenzero 

2. Multiple view geometry in computer vision book by Richard Hartley: 

This book discusses the use of geometry and algebra in image reconstruction, with applications to computer vision. In this book, Richard discusses the geometry of images and how they are processed in this area. The book covers topics such as image formation, camera models, image geometry, and shape from shading. 

The main goal of this book is to provide a comprehensive introduction to computer vision by focusing on the geometric aspects of images. This article describes a wide variety of tactics, from traditional to innovative, to make it very evident when particular approaches are being employed.  

Camera projection matrices, basic matrices (which project an image into 2D), and the trifocal tensor are all introduced, along with their algebraic representations, in this book. It explains how to create a 3D model using a series of photographs taken at various times or in different sequences.  

3. Principles, algorithms, applications, learning book by E. R. Davies: 

New developments in technology have given rise to an exciting academic discipline: computer vision. The goal of this field is to understand information about objects and their environment by creating a mathematical model from digital images or videos, which can be used to extract meaningful data for analysis or classification purposes.  

This book teaches its readers not just the basics of the subject but also how it may be put to use and gives real-world scenarios in which it might be of benefit.  

4. Deep learning for vision systems by Mohamed Elgendy: 

This book should be the go-to text for anyone looking to learn about how machine learning works in AI (Artificial Intelligence) and, fundamentally, how the computer sees the world. By using only the simplest algebra a high school student would be able to understand, they can demonstrate some overly complicated topics within the AI engineering world.  

Learn about deep learning using Python

Hands-on deep learning using Python in Cloud

Through illustrations as well as Elgendy’s expertise, the book is the most accurate yet simplest way to understand computer vision for the modern day. ~ Founder & CEO of Lantech 

5. Digital image processing by Rafael C. GONZALES and Richard E. Woods: 

Image processing is one of the topics that form the core of Computer Vision and DIP by Gonzalez is one of the leading books on the topic. It provides the user with a detailed explanation of not just the basics like feature extraction and image morphing but also more advanced concepts like wavelets and superpixels. It is good for both beginners and people who need to refresh their basics.

It also comes with MATLAB exercises to help the reader understand the concepts practically. Senior Machine Learning Developer, AltaML  Rafael C. GONZALES and Richard E. Woods wrote this book to provide an introduction to digital image processing for undergraduate students and professionals who are interested in this field.

The book covers the fundamentals of image formation, sampling and quantization, the design of analog-to-digital converters, image enhancement techniques such as filtering and edge detection, image compression techniques such as JPEG and MPEG, digital watermarking techniques for copyright protection purposes and more advanced topics like fractal analysis in texture synthesis. 

6. Practical machine learning for computer vision: End-to-end machine by Martin Görner, Ryan Gillard, and Valliappa Lakshmanan: 

Learning for Images. This tutorial shows how to extract information from images using machine learning models. ML (Machine Learning) engineers and data scientists will learn how to use proven ML techniques such as classification, object detection, autoencoders, image generation, counting, and captioning to solve a variety of image problems.  

You will find all aspects of deep learning from start to finish, including dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show how to use robust ML architecture to develop accurate and explainable computer vision ML models and put them into large-scale production in a flexible and maintainable manner. You will learn how to use TensorFlow or Keras to design, train, evaluate, and predict models. Senior IT Director at Propnex 

Further, this book provides a great introduction to deep end-to-end learning for computer vision, including how to design, train, and deploy models. You will learn how to select appropriate models for various tasks, preprocess images for better learnability, and incorporate responsible AI best practices. The book also covers how to monitor and manage image models after deployment. You will also learn how to put your models into large-scale production using robust ML architecture. The authors are Google engineers with extensive experience in the field, so you can be confident you are learning from the best. – Will Cannon, CEO, and Founder of Uplead   

7. Computer vision by Richard Szeliski:  

This book is all about algorithms and applications. This book is perfect for undergraduate students in computer science as it aims to provide a comprehensive course in computer vision. It is also known as the bible of computer vision. The focus of this book is on the algorithm, application, and techniques for image processing and recognition in CV.

It also helps one to get an understanding of the real-based applications and further discuss the implementation and practical challenges of techniques in computer vision. Co-Founder at Twiz LLC 

If you are interested in teaching senior-level courses in this subject, then this book is for you as it can help you to learn more techniques and enhance your knowledge about computer vision. 

Share more computer vision books with us 

If you have read any other interesting computer vision book, share with us in the comments below and let us help the learners to begin with computer vision.