Computer Vision

Saving lives behind the wheel: Artificial Intelligence and Computer Vision for road safety 
Aadam Nadeem
| October 31, 2022

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.  

 

saving lives behind wheels - AI and road safety
Artificial intelligence and computer vision for road safety

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/ 

 

Top 7 Computer Vision books to master your learning 
Guest blog
| October 29, 2022

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. 

Top 7 computer vision books
Top-7-computer-vision-books you must read – Data Science Dojo

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

Learning OpenCV 4 computer vision book
Learning OpenCV 4 Computer Vision with Python 3

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: 

Multiple view geometry - computer vision book
Multiple view geometry – computer vision book

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: 

Principles, algorithms, applications - computer vision book
Principles, algorithms, applications – Computer Vision book

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: 

Deep learning for vision systems- computer vision book
Deep learning for vision systems -Computer Vision book

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: 

Digital image processing - computer vision book
Digital Image Processing – Computer Vision book

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: 

Practical machine learning - computer vision book
Practical Machine Learning – Computer Vision book

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:  

Algorithm and application - Computer Vision book
Algorithm and application – Computer Vision book

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. 

Related Topics

Up for a Weekly Dose of Data Science?

Subscribe to our weekly newsletter & stay up-to-date with current data science news, blogs, and resources.