Computer Vision And The Internet Of Things: What Does It Mean For Your Company?

The technology of computer vision is becoming more and more ubiquitous. It is used in many different industries for a variety of purposes. This blog post will give you a brief introduction to the computer vision market and technology.
Computer vision is a field of artificial intelligence that deals with giving computers the ability to see and interpret the world as humans do.
It is used in many different industries such as automotive, retail, healthcare, manufacturing, and security. The global computer vision market was valued at $9.6 billion in 2019 and is expected to grow to $26.2 billion by 2024, at a CAGR of 22.3%.
The major drivers of this growth are the increasing need for process automation, rising the demand for security and surveillance, and the growing popularity of drones.

Computer Vision Market

The global computer vision market is forecast to reach USD 37.3 billion by 2025, up from an estimated USD 15.6 billion in 2020, according to a new report by Data Library Research, Inc.
The market is expected to witness significant growth over the forecast period on account of the increasing demand for automated systems across various industry verticals such as automotive, retail, healthcare, and manufacturing.
Amongst others, the automotive sector is one of the major application areas for computer vision technology. Automotive OEMs and suppliers are increasingly integrating computer vision-based advanced driver assistance systems (ADAS) and autonomous driving (AD) features in vehicles to enhance safety and improve the overall driving experience.
For instance, in October 2019, Mercedes-Benz AG launched its latest S-Class luxury sedan with 48 new driver assistance functions enabled by computer vision technology. Moreover, growing awareness about better benefits offered by computer vision-based ADAS & AD features is expected to drive market growth over the next few years.
The retail sector is another major application area for computer vision systems. Retailers are deploying these systems at various touchpoints such as point of sale (PoS), shelf monitoring, customer flow analysis, and fraud detection to gain insights into customer behavior and preferences.
For instance, in May 2018, Walmart Stores Inc., one of the leading retailers in the world announced that it would deploy AI-enabled shelf-scanning robots equipped with computer vision technology across 1,860 stores in the U.S. by the end

What is Computer Vision?

Computer vision is a field of computer science that deals with the extraction, analysis, and manipulation of digital images. It is a rapidly growing field with applications in many different areas, such as medical imaging, video surveillance, biometrics, automotive safety, and human-computer interaction.
The goal of computer vision is to enable computers to see and understand the world in the same way that humans do. This involves extracting high-level information from digital images, such as objects, faces, and text.
There are many different techniques for doing this, including image processing, pattern recognition, machine learning, and deep learning.

The history of Computer Vision

The history of computer vision can be traced back to the early days of computing. One of the earliest examples of computer vision was developed by Bell Labs in the 1960s. This system was designed to automatically detect faults in television signals.
Other early examples of computer vision include the Space Shuttle's automatic landing system, which was designed to identify and track landings sites during approach and landing.
Another early application was medical image analysis, which was used to improve diagnostic accuracy and efficiency.
The field of computer vision has seen significant growth in recent years, driven by advances in artificial intelligence (AI) and machine learning algorithms.
These technologies have enabled computers to increasingly interpret and understand digital images.
As a result, computer vision is now being used in a wide range of applications, including security and surveillance, automotive safety, robotics, and healthcare.

The different types of Computer Vision systems

There are four main types of Computer Vision systems. These are: 1. Machine learning-based systems 2. Deep learning-based systems 3. Pattern recognition-based systems 4. Model-based systems Machine learning-based systems use algorithms that can learn from data. They can be used to identify objects, classify images, and track moving objects. Deep learning-based systems use algorithms that can learn from data in a way that is similar to the way humans learn. They can be used for tasks such as facial recognition and image classification. Pattern recognition-based systems use algorithms that can identify patterns in data. They can be used for tasks such as object detection and tracking. Model-based systems use mathematical models to represent objects and scenes. They can be used for tasks such as 3D reconstruction and object recognition.

Pros and Cons of Computer Vision

There are many advantages and disadvantages of computer vision that should be taken into account when determining whether or not this technology is right for your business.

Some advantages include the ability to automate tasks, increased accuracy and efficiency, and reduced costs. However, some disadvantages include the potential for errors, reliance on artificial intelligence, and limited compatibility.

What are the applications of Computer Vision?

Computer vision is a process of extracting high-level information from digital images or videos. It’s the technology that enables computers to “see” and is used in a variety of applications, such as: • Object recognition: Identifying objects in digital images or video. This could be something as simple as identifying a specific type of vehicle in a traffic scene or recognizing a person’s face in a crowd. • Scene understanding: Determining the context of an image or video, such as whether it depicts indoor or outdoor scenes, day or night, etc. • Motion detection: Tracking objects as they move within an image or video frame. This could be used for security purposes (e.g., detecting intruders), or for tracking the movement of vehicles in traffic. • Image stitching: Combining multiple images to create one larger image, such as when creating panoramic views from individual photos.

How to get started with Computer Vision

If you're interested in learning more about computer vision, there are a few things you should know before getting started.
First, it's important to understand the basics of image processing and digital image analysis. This knowledge will give you a strong foundation on which to build more specific computer vision skills.
Next, familiarize yourself with popular computer vision software and libraries. Many of these tools are open source, meaning they're free to use and modify.
This makes them ideal for learning and experimentation. Some popular computer vision software packages include OpenCV, SimpleCV, and scikit-image.
Finally, don't be afraid to get your hands dirty with some code! The best way to learn computer vision is by implementing it yourself. There are many online resources and tutorials that can help you get started. With these tips in mind, you're ready to start learning computer vision!

Conclusion

The computer vision market is growing rapidly, with new applications and technologies emerging all the time. This article has provided a brief overview of the current state of the market and some of the most exciting new developments in the field. With so much happening in this rapidly-growing industry, it's an exciting time to be involved in computer vision.