How Does the World’s Most Powerful Raspberry Pi CM5 AI Box ‘See’ and ‘Understand’?
At Seeed Studio, we’re constantly pushing the boundaries of what’s possible with edge computing. The reComputer AI Industrial R is the world’s most powerful Raspberry Pi AI Box built to empower intelligent systems with vision AI capabilities.
In the last blog, we introduced the reComputer AI Industrial R — a powerful solution designed to bring the next level of intelligent understanding to IP camera systems. With its advanced combination of Raspberry Pi CM5 and Hailo-8, the reComputer AI Industrial R empowers cameras to not only capture high-quality video but also analyze scenes in real-time.
In this blog, we’ll begin by showcasing the impressive capabilities of Seeed’s Raspberry AI Box in terms of what it can “see.” In other words, how it processes visual information and perceives the environment around it.
Then, we’ll delve into the advanced features of the reComputer AI Industrial R, highlighting how it goes beyond just seeing to actually comprehend and interpret the visual world around it.
What it can sees
1. Real-Time Object Recognition
This demo runs the basic YOLO object detection model on the AI box, enabling it to scan images and instantly identify a wide range of objects, such as people, animals, furniture, and more. This demonstration showcases how the AI box efficiently processes visual data and provides immediate feedback, offering valuable applications across industries like security, retail, and beyond.
2. Pose Estimation
Model Zoo, a robust collection of pre-trained models designed for diverse use cases. Pose estimation tracks human body movements in real-time, allowing the system to capture and analyze human poses with accuracy. This functionality has broad applications. You can upload images or even analyze real-time video streams, enabling precise assessments of body posture, gestures, and more.
The versatility of ModelZoo ensures that the reComputer AI Industrial R can be adapted to various needs, offering customized insights for both personal and professional environments.
3. Real-Time Text Recognition – OCR Detection
The first two demos showcase models from the Model Zoo, demonstrating its capabilities in processing various tasks. The OCR model is developed by Seeed. This custom solution offers additional support for users, enabling them to perform a broader range of visual processing tasks. It allows users to execute tasks quickly and conveniently on the AI Box, enhancing overall efficiency and versatility.
4. Smart Home – Home Assistant Integration
Lastly, the reComputer AI Industrial R brings added intelligence to the realm of smart homes. Once intergrated with Home Assistant–an open-source smart home platform, the reComputer AI Industrail R can monitor environments and provide assistance in everyday tasks. For example, the camera connected to the AI box can detect when your cat enters a specific area, helping you make decisions like whether it’s time to feed your pet or not.
This smart home integration offers a seamless way to automate routine tasks, increase efficiency, and enhance the overall user experience in the future. The AI box’s ability to see its surroundings opens up a world of possibilities for modern living.
The AI Box is equipped with advanced visual capabilities that allow it to perceive its environment in a variety of ways. From identifying objects and detecting human movements to reading text and integrating with smart home systems, it captures and processes visual data in real-time. This enables the AI Box to “see” the world around it. Its ability to recognize patterns, monitor interactions, and adapt to dynamic environments is at the core of its versatile functionality.
Understanding What it Sees
When talking about vision AI, IP camera technology has come a long way. It has evolved through three distinct stages, transforming the way we use video surveillance.
Initially, cameras were only able to capture video — the basic function of “seeing.” These early systems could record footage. Then came the second stage: “seeing clearly.” With advancements in image quality, cameras became better at delivering sharp, high-resolution visuals, making it easier to monitor and analyze environments.
Now, we’ve entered the most advanced stage: “understanding” what the camera sees. This shift is where the magic happens. Think about modern applications like detecting queue lengths, counting people, or identifying abnormal behaviors. These tasks go beyond simple image capture. To perform these functions, the camera system needs to do much more — it needs to interpret the scene, recognize patterns, and make intelligent decisions based on what it sees. This is where AI and machine learning come into play, enabling cameras to not only see but also understand their surroundings.
Object Tracking: Keeping Up with Movement
Object detection alone is impressive, but what if we need to track objects as they move? With the integration of tracking algorithms like ByteTrack and DeepSORT, the reComputer AI Industrial R can track individuals in real-time across frames.
In this demo, the system detects multiple people in a scene and assigns each person a unique ID number. Even as people move around or are temporarily obstructed from view, their ID remains intact, ensuring continuous tracking. This makes the reComputer AI Industrial R perfect for applications such as monitoring security footage or optimizing store layouts based on customer movement patterns.
Detecting Abnormal Events: From Falls to Anomalous Intrusion
What truly sets advanced vision systems apart is their ability to recognize not just objects, but also specific actions or events. The device can detect unusual behaviors such as someone falling or climbing over a fence — crucial for enhancing safety and security in environments like hospitals, airports, and public spaces.
Using a simple logic system, the reComputer AI Industrial R can detect when a person falls. In this demo, the detection box changes color based on the person’s posture. When a person is standing, the box appears purple, but when they fall and their body’s aspect ratio changes, the box turns red to indicate a fall.
The reComputer AI Industrial R can also be used for more complex scenarios like fence climbing detection. By tracking a person’s movement and analyzing the height of the fence, the AI Box can capture and detect a person’s fall. But how can this function actually helped in real life situation?
Once an abnormal events was detected, alerts will be sent to relevant devices. For example, this can address the safety issues of elderly people living alone. Once the reComputer detect their fall, in almost real-time, alerts will be sent to their kids’ phones.
CLIP Model: Flexible Image Search and Automatic Labeling
The true potential of the reComputer Industrial AI goes beyond object detection and tracking. It can also run advanced models like CLIP (Contrastive Language–Image Pretraining), which allows the device to understand both text and images in a highly flexible and human-like manner.
While traditional models like YOLO need to be specifically trained for certain objects, CLIP can understand natural language descriptions. This opens up new possibilities for flexible image searches and automatic dataset labeling. For instance, you could simply type “person climbing a fence” or “yellow cat sleeping in sunlight” as shown in the demo and the system will find and labeled relevant images without needing prior training.
Automation: Beyond Detection to Action
AI-powered vision is not just about recognizing and tracking objects — it’s about taking action based on what’s detected. The reComputer AI industrial R can be seamlessly integrated with home automation systems, making it easy to set up automated responses to various events.
In a smart home scenario, you could program the device to automatically turn on the lights when someone enters a room or lock the doors if unusual activity is detected. This seamless integration with Home Assistant ensures that AI systems are not just passive detectors, but active responders.
As mentioned earlier in this blog, Seeed Studio has prepared our AI Box to better serve your needs, evolving from simply detecting to analyzing and making decisions.
For those who prefer more customizable automation, the reComputer AI Industrial R also supports integration with Node-RED, a flow-based development tool for wiring together hardware devices, APIs, and online services. In this demo, we set up a simple flow that sends a Telegram alert whenever the fall detection system identifies someone falling.
How to Use Your AI Box to See and Understand?
With a vast range of tools at your disposal, the reComputer AI Industrial R makes it easier than ever to implement AI-powered vision solutions to scenarios like smart retail, smart building, smart security and more.
To help you get the most out of your ReComputer AI R Series, Seeed has prepared another comprehensive guide — Tutorial-of-AI-Kit-with-Raspberry-Pi-From-Zero-to-Hero.
This course is designed to teach you how to harness the power of AI on the Raspberry Pi, with a particular focus on using an AI kit to perform essential computer vision tasks. Throughout the course, you’ll learn how to integrate AI into real-world IoT (Internet of Things) applications, from object detection and image classification to more complex visual recognition tasks. We will guide you step-by-step through setting up your Raspberry Pi, using AI frameworks, and deploying these models in various practical scenarios.
Looking Ahead: Audio AI and Future Innovations
With the reComputer AI Industrial R, Seeed Studio is pushing the envelope on what’s possible with vision AI. From object detection and tracking to advanced models like CLIP and automated responses, this device is a versatile powerhouse for a wide range of applications. Whether you’re looking to enhance security, streamline operations, or simply explore the cutting edge of AI, the reComputer AI R series is the ideal tool for bringing your vision to life.
While the focus of this blog was on vision AI, Seeed Studio is actively working on Sound/Voice AI. We’ve come up with the AI-powered 4-mic Array. Stay tuned for more exciting updates and blogs as we continue to strengthen our commitment to the world.
Learn more about reComputer AI R series:
Making Next Gadget | See It Live: Raspberry Pi CM5 AI Box Launch + Hands-On Tutorial Updates!
Making Next Gadget | From Pixels to Meaning: How Does Raspberry Pi CM5 AI Box ‘See’ and Interpret?
Making Next Gadget: Master AI with Raspberry Pi, Power Smart Retail Solutions – YouTube
Making Next Gadget: AI Boosted RPi for Real World Applications
Learn more about how to use your AI kit with Raspberry Pi:
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