How to calculate distance using computer vision models?

An overview of distance calculation in computer vision

Distance calculation in computer vision involves detecting, locating, and measuring the pixels between two objects in an image. Pixels are the individual units that make up a digital image, each representing a single point with a specific color or intensity value. 

To convert pixel measurements into real-world distances, calibration is key. You can think of it as using a ruler to measure something and then using that measurement to understand the size of other objects. By referencing objects with known sizes, calibration creates a link between the pixels and actual physical distances.

Let’s look at an example to see how this works. In the image below, the coin is the reference object, and its size (0.9in by 1.0in) is known. By comparing the pixel measurements of the other objects to the size of the coin, we can calculate their real-world size.

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However, distance calculation is done in a two-dimensional (2D) plane, meaning it only measures the horizontal and vertical distances between objects. This is different from depth estimation, which measures the distance of objects in three-dimensional space, including their distance from the camera.

While depth cameras can measure true depth and give more detailed spatial information, in many cases, a simple calibrated distance is enough. For instance, knowing how far apart objects are in a 2D plane works well for tasks like tracking objects or managing queues, so depth estimation isn’t needed in those situations.

Understanding how distance calculation using YOLO11 works

Next, let’s walk through how to calculate the distance between two objects using YOLO11’s support for object detection and tracking. Here’s a breakdown:

• Detecting and tracking objects: YOLO11 can be used to detect objects by recognizing them in a frame and tracking their movement across frames in a video. It assigns a unique track ID to each object, allowing the system to monitor their position and movement throughout the video.
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• Bounding boxes: Bounding boxes are drawn around the detected objects based on YOLO11’s object detection results, which define their location in the image.
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• Selecting objects: Two objects can be selected to focus on, and YOLO11 updates the bounding boxes as the objects move in real time.
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• Centroid calculation: Centroids (the center points of the two objects’ bounding boxes) are calculated based on the coordinates of the bounding box corners, representing the objects' positions.
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• Distance calculation: The centroids of the two objects can then be used to calculate the Euclidean distance between them. The Euclidean distance is the straight-line distance between two points in a 2D plane, calculated using the differences in both the horizontal and vertical directions. This provides the distance between the two objects in pixels.

It’s important to keep in mind that the distances calculated using this method are only estimates as they are based on 2D pixel measurements.

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The impact of YOLO distance estimation

Considering that calculating distances using YOLO11 is an estimate, you might be wondering: Where can this be used, and how can it make a difference? 

Since calibrations are used to arrive at these distance estimations, they are accurate enough to help in many practical situations. YOLO11’s distance estimation is particularly useful in dynamic environments, like warehouses, where objects are constantly in motion and real-time adjustments are necessary to keep things running smoothly.

An interesting example is using YOLO11 to track packages on a conveyor belt and estimate the distance between them in real time. This helps warehouse managers make sure packages are spaced properly, preventing collisions and keeping things running smoothly. 

In such cases, an exact distance isn’t always necessary. Typically, a range or threshold for the optimal distance is set, so an estimate works well for these types of applications.

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Distance Calculation in Computer Vision Applications

Various computer vision applications can benefit from calculating the distance between objects using YOLO11. In retail analytics, for example, it helps improve queue management by tracking customer positions in real time. This makes it possible for businesses to better allocate resources, reduce wait times, and create a smoother shopping experience. By dynamically adjusting staffing levels and managing customer flow, stores can prevent overcrowding and optimize the use of space.

Similarly, in traffic management, distance estimation helps monitor vehicle spacing and analyze traffic patterns. This can be used to detect dangerous behaviors, like tailgating, and adjust traffic signals to keep traffic flowing smoothly. It can help make roads safer by identifying potential issues and improving overall traffic management in real time.

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Another unique use of this technology came during the COVID-19 pandemic when it helped promote social distancing. It made sure people kept a safe distance in public spaces, stores, and hospitals, reducing the risk of spreading the virus. 

By tracking distances in real time, alerts could be sent out when individuals were too close, making it easier for businesses and healthcare providers to respond quickly and maintain a safer environment for everyone.

Pros and cons of distance calculation in computer vision

Now that we've discussed some of the applications of distance calculation using computer vision, here’s a closer look at the associated benefits of doing so:

• Automation: Vision AI can automate distance calculation tasks that would otherwise require manual tracking, reducing human error and labor costs while increasing productivity.
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• Resource optimization: By knowing the approximate spacing and proximity of objects, resources (like staff or machinery) can be optimized, bringing down wastage and improving workflows.
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• Minimal setup complexity: Once set up, vision-enabled distance calculation systems are relatively low-maintenance and require less human intervention compared to other systems that rely on manual input or complex machinery.

Despite these advantages, there are also some limitations to keep in mind when implementing such systems. Here’s a quick glimpse of key factors to consider when it comes to distance calculation using computer vision:

• Sensitivity to environmental factors: The accuracy of distance calculation can be affected by lighting conditions, shadows, reflections, or obstructions, leading to potential errors or inconsistencies.
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• Privacy concerns: Using computer vision to track objects or people can raise privacy issues, especially in public spaces or sensitive environments where personal data might be involved.
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• Complexity in multi-object scenarios: In environments with numerous moving objects, accurately calculating distances between multiple objects simultaneously can lead to confusion, particularly if they are close together or intersecting.

Key takeaways

Calculating the distance between objects using YOLO11 is a reliable solution that can support decision-making. It’s especially useful in dynamic environments like warehouses, retail, and traffic management, where keeping track of object proximity can improve efficiency and safety. 

YOLO11 makes it possible to automate tasks that would normally require manual effort. While there are some challenges, such as sensitivity to environmental factors and privacy concerns, the benefits, like automation, scalability, and easy integration, make it impactful. As computer vision continues to improve, especially in areas like distance calculation, it’s likely that we'll see a real shift in how machines interact with and understand their surroundings.

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