Application developers only need to decide how to use the information these functions provide. They don't need to know how to extract the information from the video. Vendors include Abstract Computing International, Agent Video Intelligence, BSR Labs, Cernium, Eutecus, Eptascape, IntelliVision, ObjectVideo, Survision, and VCA Technology. These vendors caution developers new to the field to have realistic expectations, however, for what video analysis can achieve.
Reliable and accurate detection and classification of color, for instance, is a highly complex task that depends as much on target illumination as analysis software. Without controlled lighting, analysis results can be variable. Increasing the processing power or camera resolution does not improve results significantly, either for color or other types of analysis.
While more processing power enables the system to support more simultaneous video channels or achieve faster results, it's unable to extract more information than a more modest processor. Available algorithms work in broad strokes, able to distinguish a person from an automobile, but typically unable to make fine distinctions such as telling a Ford from a Chevrolet.
Within their limits, though, video analysis algorithms should perform reliably in a wide range of environments, although poor lighting, bad camera angle, camera movement, reflections, and other environmental factors will have a detrimental impact. Even so, developers should expect systems in most installations to detect target objects appropriately better than 90% of the time (less than 10% false negatives), according to Cernium's Gagvani.
False positives, while more difficult to measure, should stay less than 10%. (This does not count nuisance recognitions such as reflections in a window being identified as a person.) The more controlled the environment, the better these results will be.
What these performance results suggest, and vendors point out, is that video analysis is best used to support human activity rather than serve as the entire solution. “People need to look at analytics as a tool to give humans more information to make a judgment,” said ObjectVideo's Troha. “It's a team effort.”
IntelliVision's Nathan pointed out that video systems don't work like human eyes and cannot recognize things the way people do. These systems are good at keeping track of results over long periods of time, he said. An analysis system can watch a hundred cameras simultaneously for months at a time without losing efficiency (Fig. 4). A human cannot.
While the reality of video analysis' abilities are a far cry from what Hollywood would have us believe, they can handle the job in many key applications. As cameras proliferate, opportunities to put these brains with those eyes will increase. Success hinges on setting appropriate expectations, working with vendors to speed development and integration, and cleverly combining and applying the fundamental capabilities video analysis has to offer.