AI cameras – their significance and applications in embedded vision

While the introduction of ChatGPT made AI more accessible, Devin AI, the world’s first AI software engineer launched by Cognition, is redefining the boundaries of what we could do with AI. This tectonic shift is visible in all domains including healthcare, industrial automation, education, and smart cities.

Embedded cameras are a part of this AI-driven transformation. With advancements in sensor technology and processing platforms, product developers can leverage the full potential of artificial intelligence to automate tasks that were once unimaginable. 

In this article, we introduce what AI cameras are, the role of embedded vision in elevating their performance, and the different types of AI cameras. We will also cover how TechNexion is making its mark in the field of AI-enabled vision by building world-class cameras.

What is an AI camera?

The old way of image analysis was manual. It involved capturing images using a camera which were then monitored and interpreted by a human sitting in a control room. We have come quite far from that. While many applications continue to require supervision, modern vision-enabled systems utilize the power of AI, ML, and DL (deep learning) to take intelligent decisions.

An AI camera comes with the capability to capture images that are AI-ready, or in other words, can be analyzed by machine learning and artificial intelligence algorithms for taking actions.

AI cameras are used in various vision systems such as robots, drones, sports broadcasting & analytics devices, and smart surveillance equipment to perform functions such as the following:

  • Object recognition
  • Intelligent path planning
  • People detection
  • Object classification

Advantages of an AI camera system

AI-enabled cameras have automated several tasks that were previously human-dependent. In addition, they have reduced the time it takes to perform certain tasks significantly, let us understand this using two examples.

  1. Traffic monitoring cameras

Traditionally, the number of vehicles had to be counted with the help of dedicated personnel. Now, thanks to deep learning and computer vision algorithms, this task can be completely automated.

Traffic monitoring camera

Now consider an automated weeding robot that can automatically identify bugs and weeds with the help of embedded cameras and AI. It can also destroy the weeds by spraying pesticides by accurately locating their position. Doing this manually is a tedious process and could consume a lot of time.  Attempting to do this algorithmically is also quite difficult, requiring advanced image analysis and pattern recognition.

AI cameras make this a breeze with their ability to capture clear images that can be readily analyzed by AI algorithms.

Some of the other advantages of an AI camera system include:

  • With the deep learning capability, they can learn over time and perform better as they collect more data and insights.
  • The data collected using AI cameras can be used for training newer AI and deep learning models.
  • Despite the higher initial capital investment, AI cameras can reduce costs over time owing to an increase in productivity and speed.

How embedded cameras elevate AI-enabled vision systems

Capturing AI-ready images might need the camera to have special capabilities. This is very application-dependent, meaning that every device can have different requirements.

For instance, consider a harvesting robot that uses AI to identify fruits and vegetables that are ripe and ready to be harvested. This demands the camera to come with a high level of color accuracy. Similarly, in an autonomous industrial robot used in a warehouse, the accuracy of 3D depth sensing must be high to ensure that it can do the localization and path planning without any error.

An autonomous industrial robot

Embedded cameras with the right features can enhance the performance and accuracy of AI-enabled vision systems. In addition to color accuracy and depth perception, below are certain other features that are most required in camera modules used in AI imaging systems:

  • Resolution: As in the case of ordinary camera modules, resolution is key in AI cameras too. The more detailed the image, the easier it is for the AI program to interpret it.
  • High frame rate: This is usually needed in applications that involve fast motion of target objects. Having a high frame rate helps to eliminate motion blur in such scenarios.
  • High dynamic range: This is mostly required in outdoor systems such as agricultural robots, patrol robots, and autonomous tractors where the camera is continuously exposed to bright sunlight.
  • Global shutter: This is required if the output comes with rolling shutter artifacts.
  • Near Infrared performance: Many AI cameras must operate at night. Capturing images in the visible spectrum might not be practical in such situations. Having a camera optimized for image capture in the Near IR region can be a game-changer here.

There could be several other features needed in an AI camera depending on the specific needs of the system. However, the above are the most important.

Types of AI cameras used in different applications

AI cameras find applications in a wide variety of domains including sports, medicine, industrial automation, and more. Here, we look at the types of AI cameras based on where and how they are used.

AI security cameras

Smart surveillance camera systems are widely used today for monitoring a perimeter. With the help of AI, they can automatically detect the presence of any unwanted object or people. They can also send instant alerts to security personnel for immediate action. This type of AI camera is used in locations such as military bases, large mines, and country borders. 

Related: Ensuring Perimeter Security Using Camera-based Smart Surveillance Systems

An AI-enabled smart surveillance camera

AI soccer cameras

Automated sports broadcasting and analytics is an upcoming AI-enabled vision application. In these systems, high quality cameras are installed in sports arenas to capture matches. These matches are then broadcast on the web using a streaming platform. While professional matches are captured using high-end crew and camera setup, automated sports broadcasting is typically done for amateur matches where cameras are unattended and do not need a dedicated team to operate.

The role of AI lies in interpreting the video data captured for analyzing player performance and ball position. AI programs can accurately locate the position of the ball throughout the match for effective ball tracking. This data along with player information is used to assess player capabilities, which in turn is used by coaches and team managers for player selection and team management.

Learn more about how embedded cameras enable AI-based sports analytics and broadcasting here.

AI dash camera

AI dash cameras are used for in-vehicle surveillance primarily to ensure driver and passenger safety. According to the National Highway Traffic Safety Administration, there were 684 deaths in the year 2021 due to drowsy driving. In-vehicle surveillance enabled by AI can offer a solution to this problem. With the ability to detect drowsiness before the driver falls asleep, AI-enabled in-vehicle surveillance cameras can enhance safety by many folds.

AI video camera

AI video cameras are used in modern video surveillance applications where the camera can automatically zoom in on the speaker. These camera systems use a combination of audio and visual signals to identify the speaker among a group of people.

AI in traffic monitoring

Traffic monitoring cameras are used for automatic license plate recognition, vehicle counting, and crowd analytics. Experimentations are going on currently to use these cameras to identify drivers and passengers using AI-enabled facial recognition. The quality of the camera is of utmost importance here since even a tiny loss in detail can lead to biased and inaccurate outputs.

TechNexion – building camera modules for the AI era

TechNexion designs and manufactures cameras for next-gen AI-based vision systems such as robots, drones, autonomous tractors, smart traffic systems, and more. With features such as high resolution, high dynamic range, global shutter, and near infrared optimization, our cameras are well-suited for any high-end vision system.

For example, VLS3-AR0822-CB is a 4K HDR camera that is optimized for superior performance in the Near IR spectrum.

The key features that make it perfectly suited for AI vision systems are:

  • Based on the AR0822 sensor from Onsemi with a resolution of 8MP.
  • Designed for low light performance.
  • C Mount, offering you the flexibility to choose from a wide variety of lenses.
  • FAKRA connector ensures sturdiness and resistance against shocks and vibrations.

TechNexion’s portfolio comprises several other products that come with the ability to capture images suitable for AI vision systems. Visit the embedded vision cameras page to learn more about our camera solutions.

VLS3-AR0822-CB Kit

FPDLINK-III IP68 Camera with onsemi AR0822 8MP 4K Color Rolling Shutter with ISP + IR-Cut Filter with C Mount Body

In stock Out of stock On backorder

VLS3-AR0822-CB

  • onsemi AR0822 8MP Rolling Shutter Sensor
  • 4K HDR Imaging Capabilities
  • Near Infra-Red Enhancement for Outdoor Applications
  • Designed for Low Light Applications
  • C-Mount for Interchangeable Lenses
  • FAKRA Automotive Connector
  • Plug & Play with Linux OS & Yocto
  • Comes with VizionViewer™ Configuration Utility and VizionSDK

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