While the history of digital cameras goes back to the late 1960s, it was not until the late 1990s that they saw mainstream adoption in the industrial sector. With the advent of machine vision, the late 2000s witnessed a huge leap in industrial automation with factories across the world being equipped with cameras that can monitor production lines. As camera technology advanced, high frame rate cameras came to the fore which made high-speed monitoring possible, which in turn increased production capacity and the overall throughput.
However, machine vision cameras were often bulky and depended on specialized software or the cloud for image processing. This made on-field and real-time analysis challenging. Embedded vision made a breakthrough here by making onboard processing of images possible using on-device processors. These processors were not only compact but offered sufficient performance to meet the demands of modern vision systems.
The next revolution in embedded vision and imaging technology is AI (Artificial Intelligence). Advancements in AI algorithms have enabled automated analyses of image and video data to derive actionable insights. These insights are used as inputs for the vision system to perform certain tasks.
In this blog post, we explore the exciting world of AI and its implications in embedded vision. We will learn different AI-based embedded vision applications and what the future of AI in embedded vision looks like.
The evolution of artificial intelligence
Artificial intelligence is a branch of computer science that develops systems and technologies to replicate human intelligence. It uses techniques like machine learning to ingest data to train the system or device to perform cognitive actions like humans do.
It was in 1955 that the word “artificial intelligence” was first used by John McCarthy in a summer workshop at Dartmouth College. There were significant strides in the field of AI in the coming decades. It gained public attention in the late 90s as many companies started releasing AI engines or systems that could interact (and beat) humans. The Deep Blue Computer developed by IBM that beat chess world champion Gary Kasparov is a notable example.
In the 2000s, AI started finding applications in various fields such as healthcare, scientific experiments, robotics, education, etc. It took several more years for it to find its way into embedded vision. It was not until the last 5 years that many large companies and startups came up with AI-based use cases in embedded vision. With rapid advancements in computer vision and AI, product developers can combine cameras and AI to power imaging in smart cities, industrial automation, medical applications, retail stores, and more.
How is artificial intelligence used in embedded vision?
Embedded vision uses camera modules to capture images and videos. This image data is then transferred to a host processor. The processor then carries out the necessary image processing tasks to give the final output.
Modern vision systems like robots and autonomous tractors must make intelligent decisions autonomously. This requires the camera system to have the ability to run and process AI algorithms. While the processor takes care of this, the camera should be capable of delivering the necessary images and videos to the processor with the desired quality.
For example, VCI-AR0234-CB from TechNexion is a global shutter camera that can help robots capture images without any rolling shutter artifacts. Without artifacts, AI algorithms can process the data to help the robot with obstacle detection and autonomous navigation.
Depending on the application, the specific function of AI will change. We will go deep into the applications of AI in embedded vision in the next section.
Embedded vision applications of AI
AI has opened an entirely new horizon in imaging technology. It can be said that all the key industries directly or indirectly benefit from using embedded cameras. In this section, let us look at some of the notable applications of AI in embedded vision systems.
Robotics
Robotics is by far the most impacted domain by the advancements of AI and embedded vision. Robots of the past used guided navigation. However, modern-day robots are intelligent enough to do tasks such as:
- Localization: Creating a local map of its surroundings by capturing images and videos.
- Path planning: Planning the most optimal path from one point to another.
- Obstacle detection: Automatically detect obstacles to stop and change the path accordingly.
All these tasks require cameras. While cameras capture the necessary image data, the onboard processing of AI models acts as the brain of the robotic system. This analysis further helps the robot with localization, path planning, and obstacle avoidance.

An agricultural robot
Some of the different types of camera-enabled robots include:
- Agricultural robots – used for harvesting, plowing, sowing, spreading fertilizer, etc.
- Warehouse robots – used for carrying materials, picking and placing objects, cleaning, etc.
- Delivery robots – used for last-mile delivery of packages.
- Patrol robots – help with 24×7 security and surveillance.
- Robotic arms – they are used for lifting and placing objects.
- Service and infotainment robots – these robots are used as human companions and assistants in homes, hotels, restaurants, retail stores, etc.
- Telepresence robots – used for remote communication and operation.
Patient monitoring
Early-day vision-enabled patient monitoring systems involved medical personnel watching the patient 24×7 in a remote surveillance room. However, technology has gotten better, eliminating the need for manual monitoring. Wondering how that’s possible?
It is by combining the power of AI and embedded vision.
Today, AI models can automatically detect the movements and falls of the patient using the 24×7 video feed from the camera system.
For instance, consider a patient under remote monitoring in a hospital without a personal attendant. If the patient happens to fall from the bed or during a walk to the toilet, AI cameras can detect it and send an instant alert to the medical staff for immediate attention.
This saves a ton of time for the staff and enables them to focus on other core tasks. It also gives peace of mind to the patients and their loved ones since no undesirable incident goes unnoticed.
Sports analytics and broadcasting
Embedded cameras made it possible to capture and broadcast amateur sports matches. These cameras are installed in sports arenas to automatically capture matches without the need for a personal camera attendant or crew. This offers the players’ loved ones the ability to watch matches in the comfort of their homes.
But that’s not all. Artificial intelligence takes things to the next level by analyzing the video data to derive insights on player performance, playing patterns, ball trajectories, etc. Coaches and team managers can use this information to make game plans and modify on-field tactics. This technique is used in various sports and games such as soccer, basketball, golf, tennis, etc.
Learn more about how embedded vision is changing sports analytics here: Embedded Vision in Sports Analytics – Relevance and Applications
Digital signages
Digital signages are not just display boards anymore. Today, retailers and shop owners use it as a personalized advertising space. These AI-based smart signages are installed in retail stores, metro stations, bus depots, railway stations, etc.
Cameras installed on these signages continuously capture videos that are fed into the AI model to identify the demographic characteristics – such as age, gender, color, etc. – of the person in front of them. The signage then shows a personalized ad that is suitable for the demographic segment identified.
Explore more about how smart signages are changing offline advertising: Revolutionizing Offline Advertising With Smart Signages and Embedded Vision.

A smart signage
Autonomous tractors
Modern tractors used in agricultural lands can navigate autonomously and analyzing crops with the help of AI and cameras.
This technique is used for purposes such as:
- Automated weed and bug detection
- Identifying ripe fruits and vegetables
- Plowing and sowing
- Accurately spreading fertilizer
For the cameras used in the tractor, features like color accuracy, high dynamic range, resolution, frame rate, etc., are extremely critical.
Related: Embedded Vision – Elevating Outcomes in Precision Farming

An autonomous tractor
The future of AI in embedded vision
Though AI has transformed the way we use image and video data, its accuracy levels have been questioned. The fact is that an AI model is only as good as the data it is trained on. Hence, as these systems collect more data, their cognitive abilities will also improve, which in turn will result in higher accuracy. The future will see AI-based vision systems improving in accuracy, which could lead to more mainstream adoption of AI in embedded vision applications such as automated medical diagnosis.
This also requires camera technology to become more advanced. For example, some applications might require extremely high frame rates at medium to high resolution. This will be possible only if the camera interface and processor can support the necessary bandwidth.
With AI becoming more ‘accessible’, other applications like autonomous cars will also become more common. Today, only a limited number of companies are conducting research and testing on autonomous cars. With high-quality cameras and superior AI models, the sector is more likely to become a level playing field for large companies and startups.
TechNexion – cutting-edge cameras for the AI era
TechNexion’s camera solutions are built for the modern era. Our recent years have been focused on extensive research and development to design cameras that can meet the needs of even the most demanding AI-based vision systems. Some of the key features of our camera modules include:
- High resolution and high frame rate
- Rolling shutter and global shutter variants
- Night vision and low light imaging capabilities
- Industrial-grade interfaces and connectors
- High temperature resistance
- IP68-rated enclosure
- USB, MIPI, and FPD-Link III variants
In addition to this, we work closely with our customers to make sure our cameras are integrated into their systems without any issues. Learn more about our embedded cameras here to get started with your camera integration journey.