What should you consider when setting up cameras for Vision AI

A good Vision AI application does not only depend on the AI model, but also on how the camera is positioned and configured. Camera position, viewing angle, distance, lighting, lens choice, frame rate and compression all determine how much useful visual information the model receives.

Even a powerful AI solution can perform less effectively if the relevant object appears too small, blurred, poorly lit or from an unsuitable angle.

Camera position

The position of the camera determines which parts of an object are visible. A camera mounted too high, too low or too far away from the detection area may miss important visual features. For an application that only needs to count people, a different camera position may be suitable than for recognizing personal protective equipment or small objects. The camera position should therefore always match the application.

Viewing angle

An object can look very different from different angles. If the angle is too steep, faces, clothing, vehicles or safety equipment may only be partially visible. Perspective also matters. Objects close to the camera may appear large, while objects further away can quickly become much smaller. A good viewing angle helps ensure that the relevant object is as complete and recognizable as possible.

Distance to the detection area

The further an object is from the camera, the fewer pixels it occupies in the image. This is important because different applications require different levels of detail.

Detecting a person may be possible at a greater distance than:

• recognizing a safety helmet;
• classifying a specific vehicle type;
• detecting small products;
• reading a license plate.

The distance between the camera and the detection area should therefore be determined by what the AI actually needs to recognize.

Lighting

Even good cameras and AI models need sufficient usable light. Challenging conditions include:

• strong backlighting;
• large differences between bright and dark areas;
• reflections;
• night scenes;
• rapidly changing lighting conditions.

Higher resolution does not automatically compensate for poor lighting. The quality of the light is therefore often just as important as the number of megapixels.

Lens choice

The lens determines how much of the scene is visible and how large objects appear in the image. A wide field of view provides more overview, but individual objects may appear smaller. A narrower lens can provide more detail at a greater distance, but covers a smaller area. Choosing the right lens is therefore a balance between: overview and detail.

Frame rate

Frame rate indicates how many images are captured per second. For slow-moving scenes, a lower frame rate may be sufficient. For faster movement, such as vehicles or production processes, more frames may be needed to capture enough useful moments. More frames also mean more data to process. A higher frame rate is therefore not automatically better.

Video compression

Video compression is used to reduce bandwidth and storage requirements. However, excessive compression can remove visual detail or introduce artifacts. An image may still look acceptable to a person while small features become more difficult for an AI model to recognize. Compression should therefore not only be optimized for storage and network load, but also for the analysis that needs to be performed.

What level of detail does the application require?

This is ultimately the most important question. A people-counting application has different camera requirements than:

• PPE detection;
• product recognition;
• license plate recognition;
• vehicle classification;
• fire and smoke detection.

Do not start with the camera specification. Start with the use case.

First ask:

> What exactly does the AI need to recognize, and under which conditions?

Only then should you define requirements for position, lens, resolution, frame rate and other settings.

Better image quality also has a technical cost

Higher resolution and higher frame rates can provide more useful information, but they usually also require more:

• network bandwidth;
• storage capacity;
• processing power;
• hardware capacity.

The best configuration is therefore not the one with the highest specifications, but the one that provides enough image quality for the application without unnecessary technical overhead.

A good Vision AI installation is ultimately the combination of the right AI model and the right camera image.