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May 08, 2008

Challenge of False Positives

[From The Key Challenge for Video Analytics : IP Video Market Info]

John Honovich points out the importance of false positives. A false positive arises when a smart camera system warns operators of a situation, when the situation is normal. This problem arises when cameras move out of laboratories into real world conditions where weather and external stimuli, such as birds, make video analytics difficult.

Here are some interesting snippets:

Making sure you only trigger when someone is violating is hard because there are so many factors that might set off the analytic besides a valid adversary. To a computer, rain, dust and snow can all generate a similar form to a human being. Quick changes in light or the movement of water (waves) can also generate such forms. A camera that shakes because of the wind or issues with the mounting or installation also can trigger such alerts. The hard part in such analytics is to make sure that these alerts can be eliminated. This is a key metric in testing and differentiating between analytics.

False positives drive up the cost of systems. . . . While it is better operationally to centrally manage alerts, if the system generates dozens or hundreds of false alerts per day, the costs can become prohibitive. Let's say an 'intelligent' camera generates 5 false alerts a day at the cost of $1 per alert (the unit cost to pay a monitor to assess). That's $5 per day, over $1,800 per year and about $10,000 for a projected 5 year lifecycle. If you dozens or hundreds of cameras, this hidden operational cost can be in the millions. And this is not theoretical. This is the feedback you will hear time and again from real world deployments. It's widely accepted that this is improving but it is still the major factor in duing your due diligence in analytics.

Posted by rshah at May 8, 2008 01:21 PM

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