The integration of artificial intelligence with embedded vision systems has led to a paradigm shift – the effects of which can be seen across various industries. Vision applications powered by AI,such as autonomous vehicles, automated surveillance systems, and many others attest to the transformative potential of this convergence.
As the complexity and sophistication of AI vision systems continue to increase, the need for multi-camera systems in place of a single camera is becoming increasingly evident. A multi-camera system offers a multitude of benefits such as enhanced coverage, improved accuracy and efficiency, and the ability to gather more data.
In this article, we will explore:
- The differences between single and multi-camera systems.
- The advantages of having a multi-camera system.
- The role of multi-camera systems in elevating AI-enabled embedded vision systems.
Single Camera vs. Multi-Camera Systems
In embedded vision systems, single-camera and multi-camera systems, both offer unique advantages and tradeoffs. Let’s take a look at them in detail here.
As the name suggests, a single-camera system uses a single-camera module to capture images and videos. A multi-camera system, on the other hand, uses multiple camera modules to capture multiple images and videos simultaneously or at different times. They can be used to capture images and videos from different angles, or each camera can be dedicated to capture different types of images. For example, a multi-camera system can have separate infrared, RGB, and depth-sensing cameras.
The architecture of individual cameras on both systems remains the same. They all come with an image sensor, lens, interface, baseboard, and processor. However, a single-camera system may use larger image sensors and more advanced lenses compared to the individual cameras in a multi-camera system, as the focus of a single-camera system is on capturing high-quality images from a single perspective.
Single-camera systems, naturally, are more cost-effective than multi-camera systems. This is because multi-camera systems require more cameras (sometimes of different types). Plus, the increased complexity of integration, setup, and calibration of multiple cameras can add to the overall cost. Lastly, the need for high-end processors (NVIDIA Jetson, for example) to handle the data from multiple cameras can also drive up the cost.
However, even though single-camera systems seem more budget-friendly and easier to manage, they do have their limitations. For instance, a single-camera system cannot capture images from multiple angles simultaneously. This makes it difficult to get a complete view of the scene or subject being photographed.
Other limitations of single-camera systems include limited field of view, inability to use stereo pairs for depth perception, and potential blind spots. This can prove to be a hindrance in AI vision applications, such as autonomous vehicles, shopping systems, or smart traffic management systems.
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Why AI Vision Systems Find a Multi-Camera System Useful
AI vision applications require high-quality imaging so that the AI and ML algorithms are able to interpret and analyze image data without fail. In addition, capturing data from multiple angles and for different purposes is only possible with multi-camera systems. Here, let us look at the reasons why AI-enabled embedded vision systems would find multi-camera systems a better choice.
Enhances Accuracy
One of the biggest advantages of using a multi-camera system is its ability to capture images from multiple angles simultaneously. This allows for a more comprehensive view of the scene or subject, providing more information and data for AI algorithms to process. This ultimately leads to enhanced accuracy in object detection, tracking, and recognition.
This can prove highly beneficial in applications such as autonomous shopping systems. A multi-camera system can accurately track and identify items in a shopping cart from multiple angles. This reduces the chances of errors or false detections.
Similarly, a multi-camera system in an autonomous vehicle can provide a more accurate and robust view of the surrounding environment. This can help improve the accuracy of object detection and tracking, making it safer for autonomous vehicles to navigate through complex environments.
An autonomous robot
Increases Field of View
Another benefit of implementing a multi-camera system is its increased field of view. Multiple cameras can be placed strategically to have a wider area covered compared to a single camera with a limited field of view.
This is especially useful in new-age AI vision systems such as smart perimeter security systems. Thanks to multi-camera setups, AI algorithms can autonomously analyze a larger area and detect any suspicious activity with greater accuracy. The AI can then send alerts and trigger actions, providing enhanced security and surveillance.
This benefit of an increased field of view is also applicable in smart traffic management systems. Multiple cameras can be installed at different locations along the roads to monitor traffic flow and detect any accidents or congestion in real time. This allows for better traffic control and efficient use of resources.
Autonomous Vehicle
Prevents Lens Distortion
To have an enhanced field of view, some prefer wide-angle or fisheye lenses instead of a multi-camera system. This is usually done to save cost and time spent on integration. However, these types of lenses can cause distortions in the captured image.
Distortion in optics refers to the variation in magnification that occurs from the center of the lens to its edges. This phenomenon causes the image to appear stretched or compressed. Distortion makes it challenging to analyze image and video data accurately using AI algorithms.
Thus, to prevent this issue, it is recommended to use multi-camera systems rather than relying on a single wide-angle or fisheye lens camera. This ensures that the captured image is accurate and free from any distortions, providing more reliable data for AI analysis.
Offers Versatility
A multi-camera system also offers more versatility in terms of the use of individual cameras. Each camera can be configured differently, allowing for a wider range of data to be captured and analyzed.
For example, one camera can be used for normal image capture and the other one can be used for capturing infrared or depth information. This allows for a more comprehensive and accurate analysis of the scene, providing a better understanding of the environment.
Similarly, one camera can be used for non-AI tasks like capturing images or recording videos and the other one can be specifically used for AI purposes such as object identification. This allows for a more efficient and accurate data-gathering process. Many robots, drones, and autonomous vehicles leverage multiple-camera systems in this manner.
AI-Based Embedded Vision Applications of Multi-Camera Systems
Multi-camera systems are becoming increasingly popular in AI-based embedded vision applications. These systems provide a wide range of benefits such as improved accuracy, faster processing speed, and increased flexibility. The applications include:
Autonomous Mobile Robots
One of the most promising applications of multi-camera systems is in autonomous mobile robots. These robots use multiple cameras to analyze their surroundings and make decisions based on the data gathered.
For example, an autonomous vacuum cleaning robot may use a multi-camera system to map out the room and avoid obstacles. This allows for more efficient cleaning and prevents the robot from getting stuck or damaging furniture.
Sports Broadcasting and Analytics
An exciting use case for multi-camera systems is in sports broadcasting and analytics. By using multiple camera modules in a single enclosure, automated sports broadcasting cameras can effectively capture player and ball movements.
Additionally, these systems can also be used for sports analytics such as player performance analysis and motion analysis. This provides coaches and teams with valuable insights to improve their performance.
This can prove especially beneficial for small or amateur teams who may not have the resources to hire multiple camera operators or invest in expensive broadcasting equipment. Plus, with the analytics gathered from the multi-camera system, these teams can make data-driven decisions to improve their strategies and gameplay. This can help draw in more viewers and sponsors and increase their financial standing within the industry.
Autonomous Shopping Systems
Autonomous shopping systems, also known as cashier-less or checkout-free stores, are becoming increasingly popular with the rise of e-commerce and mobile technology. These systems use a combination of sensors, cameras, and artificial intelligence to track customers’ movements and purchases within a store.
This eliminates the need for traditional checkout processes, allowing customers to simply pick up items they want and leave the store without having to wait in line or interact with cashiers. E-commerce giant Amazon has taken this concept far ahead with their Amazon Go stores, where customers can simply walk out with their items and are automatically charged through their Amazon account.
Not only does this create a more convenient shopping experience for customers, but it also streamlines operations for retailers by reducing labor costs and human error. Additionally, with the data collected from these systems, retailers can gain numerous valuable insight into customer behavior and preferences, allowing them to tailor their products and services to better meet the needs of their target audience.
Autonomous Drones
Lastly, autonomous drones with multi-camera setups and AI capabilities can be used for several purposes across different industries. In the retail sector, they can be utilized for inventory management and stock monitoring. They can ensure that products are always in stock and available for shopping. This not only saves time and resources but also improves accuracy and efficiency.
In agriculture, AI-based autonomous drones can be used for crop surveillance and analysis, providing farmers with valuable data on soil conditions, water levels, and plant health. This information enables them to make informed decisions about irrigation, fertilization, and pest control, leading to higher yields and healthier crops.
In addition, autonomous drones with AI-vision systems and multi-camera setups can also be used for disaster response, perimeter security, and environmental monitoring.
An autonomous drone
TechNexion’s Contribution to AI-Enabled Vision Revolution
TechNexion stands at the forefront of the AI-enabled vision revolution, offering cutting-edge camera solutions that drive innovation and empower organizations to harness the full potential of AI in vision technologies.
With a focus on research and development, TechNexion delivers solutions tailored to the evolving needs of embedded vision applications, ensuring optimal performance and reliability. Learn more about our embedded cameras here and get started on your own AI-enabled vision journey with TechNexion.
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