Video analytics performance is all about accuracy of detection.
Any technology requiring calibration or tuning has an accuracy problem. I’m surprised this isn’t mentioned more often.
Why do you need to tell a system what the size of a human or a vehicle is before it will work accurately? The reason is simple: The technology can’t distinguish humans and vehicles from other false alarms accurately enough.
A bird doesn’t look much like a person. A tree branch doesn’t either. Humans don’t have a problem telling the difference. But this is what calibration is doing – it is trying to eliminate these kinds of false detections.
The only reason that VideoIQ’s technology needs no calibration or tuning to work reliably in even the toughest outdoor environments is because it is accurate enough. The difference in accuracy is astounding compared to all others. It’s not just a little bit better. You won’t often find such a big discrepancy in a field with so many players.
Even the best of the best analytics products require size calibration to work reliably. Comparing uncalibrated systems against the VideoIQ iCVR shows how dramatically better the accuracy is. Even the best systems aren’t close.
When it comes to comparing advanced motion detection systems (http://spotonsecurity.com/2009/02/06/the-big-video-analytics-lie/), that sometimes try to pass themselves off as video analytics, the contrast is even more dramatic. They are unusable outdoors without calibration. That’s how bad their detection accuracy is.
Eliminating the need for calibration and tuning is not just about ease of set-up. That’s a huge benefit, but accuracy becomes an issue everywhere: Where you can use it, who can install it, how many problems will it generate.
For example, over a year ago, one of the largest video analytics companies advertised partnering with a company who wanted to use their technology for protecting a particular residential and commercial application. It was supposed to be a nationwide roll-out. There was one limitation: The system needed to be installed by untrained technicians and therefore calibration could not be used. So, they limited the range of detection, turned down the sensitivity and tried to detect only static scenes, mostly areas of cement – not where other moving objects might be present, such as bushes or trees, etc. In other words, just looking at an area of cement with everything else masked off.
Even those limitations weren’t enough. Shadows from nearby trees would often fall into the detection areas. Reflections from the sun or headlights would cross into the regions of interest. Leaves, paper or dead branches would cause false alarms. After some extensive testing, it became obvious the technology simply wouldn’t work.
The question isn’t how well does the technology work when installed and continually re-tuned by a trained professional, but how well it performs when anyone uses it.
It all comes down to detection accuracy.
