Camera Positioning Key to Video Analytics

•Published Apr 14, 2009 03:38 AM

PUBLIC - This article does not require an IPVM subscription. Feel free to share.

A routinely overlooked but key aspect to using video analytics is camera positioning. Not simply an implementation issue, camera positioning requirements can have a significant impact on the cost and design of video analytic systems. This is true whether you are analyzing license plates, people, faces or intruders.

[This is an excerpt from the premium Guide: "How to Select Video Analytics"]

In the Fine Print

In the user's manual of almost any video analytics systems are guidelines and requirements about how cameras need to be positioned. They almost always include points about avoiding direct exposure to sunlight and minimizing the angles of incident from the camera to the target. If you ignore these directions, performance can suffer dramatically.

Do This Before Buying

You need to factor this in during the design phase. If you wait until deployment, it can become a very significant problem. You may realize that you need to:

Move an existing camera
Add a new camera of the same type
Add a new, more expensive, camera

At this point, the cost will go up, often dramatically. Worse, you may realize that logistical barriers exist (such as not being allowed to put cameras in places the video analytic system requires).

If you do this before the project, sometimes it will stop the project from being deployed. However, if you do this during the project and hit a problem, not only may the project fail but the end user can look bad and the integrator can lose a lot of money.

Why This is the Case

Handling a variety of lighting conditions or different angles can dramatically increase the computing resources needed to analyze video. Even if technically possible to perform such adjustments, it's often not feasible to implement them in commercial products because of the increased hardware cost this requires.

While cost of computing resources will certainly continue to fall and vendors will make improvements, these elements will continue to be an important constraint on video analytics for years to come.

Practical Examples of This

While different types of video analytics have specific requirements, you can certainly see the same general pattern of requirements for lighting and angles. Here are resources for 3 different video analytics:

Impacting Test Data

Be careful about the accuracy of test data as the tests may be performed with camera positioning that is unrealistic for real world deployments. For instance, see this image example from i-LIDS [link no longer available]:

It's very unlikely that a camera could ever be deployed in this position. One, placing a camera on the exterior of a facility risks the theft or destruction of the camera. Two, placing a pole near a fence is a security risk. Three, often it's not even possible to place a camera in such a position because there's a road on the outside.

This position is misleading because it provides a perfect, unobstructed direct view of the target. This will result in better performance than the type of positioning that real world deployments require.

As such, test data or performance expectation needs to be evaluated in the context of the deployments that can be achieved.

Industry insights delivered to your inbox weekly.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

See this image of an actual deployment at the Vatican (from ioimage [link no longer available]):

The camera positioning is not theoretically ideal but given the layout probably the best that can be done.

Conclusion

Understanding camera positioning requirements up-front is key. Make sure the design reflects these constraints and that you can afford and are able to make any modifications that need to be done. By doing so, the probability of an optimally functioning system with minimal surprises will be maximized.