Camera Positioning Key to Video Analytics
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
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.
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.
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.