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Security and Machine Vision Merge in the Infrared
by Winn Hardin, Contributing Editor - AIA Posted 09/27/2007
This is Part 2 in a series of articles on Infrared (IR) imaging. This article looks at the growing relationship between security and machine vision as illustrated by new data fusion, global coordinate registration, and visualization techniques that build on similar techniques used in robot vision. Part I looked at how integrators are using advanced filtering systems and contour geometry to enhance industrial IR inspection systems.
Until recently, when you thought security, you didn’t think machine vision. Then came 9/11, and with it, a whole new worldview.
Security isn’t just to counter corporate espionage or a checklist item for a lower insurance policy premium. Security has to be effective. The days of simply putting up a number of cameras with the lowest possible labor investment is no longer a viable answer for chemical, financial or technology companies, or for national civilian and defense infrastructures.
Today, security systems have to be able to detect intruders at a distance that will actually allow preventive measures to be taken. Arresting the culprit after the terrorist bomb has exploded is no longer a hedgeable position because the likelihood of such an incident has increased considerably.
Now that security faces the same efficiency metrics as say, industrial production in which defects lead to unacceptable loses, security is looking to machine vision and its methods to increase the capability of a human security guard in consideration of his or her average focused attention span.
Why Security Likes IR
‘‘If you’re in a security environment, and you’re trying to watch and monitor things,’‘ Francisco continues, ‘‘you want to proactively react rather than forensically react to determine what you would do different next time. To proactively react, detection range is a necessity, and visible surveillance can’t see far at night without giving itself away through active illumination.’‘ L-3 Communications Infrared Products, previously Texas Instruments, then Raytheon before being acquired by L-3, produces uncooled long wave infrared (LWIR) sensor technology. L-3’s Cincinnati Electronic facility produces cooled mid-wave infrared (MWIR) systems for the U.S. military used mainly for Littoral environments where water meets land and fog and rain are common.
Visible CCD cameras have been the mainstay of security applications because they have historically been cheaper than IR cameras, however, visible light cannot penetrate fog, smoke and other obscurants with the efficiency of longer-wavelength IR radiation. The same principle also gives IR cameras the ability to see through thin films and coatings, such as paint used to obscure marks on shipping containers or vessels, according to SUI Goodrich Corporation’s Douglas Malchow, manufacturer of near infrared (NIR) and short-wave infrared (SWIR) InGaAs cameras.
This ability to penetrate over distances can actually reduce the total cost of ownership for a security system when comparing IR cameras to visible cameras. At night, passive uncooled LWIR cameras can see 3000 ft or more without the need for active illumination while visible cameras can only see about 200 ft with active illumination. Uncooled LWIR cameras may cost more than a simple CCTV camera, but 2000 meters of fence line would require 5 to 10 times the number of visible cameras compared to one LWIR camera, in addition to the poles, wires, monitors, illuminators and personnel needed to support the extra visible cameras.
‘‘Yes, the purchase price of an uncooled LWIR camera is higher [than CCTV], but the cost per foot of surveillance goes down as you move to passive thermal cameras’‘ notes L-3’s Francisco.
True Data Fusion for Security
Data fusion, or the intelligent combination of different sensor data into a data set that represents more than the sum of the parts, has traditionally been confused with data overlay with the result of more hype than benefit. Data overlay is simply where two fields of view are closely matched and displayed simultaneously.
Companies like L-3, however, are developing true data fusion algorithms that evaluate each scene on a pixel by pixel basis, and displays only the pixels from sensors that have the best data compared to all the rest of the sensors. Instead of simply overlaying data, the new methods make value judgments about the weighted importance of each pixel’s piece of data and display it in an intuitive representation.
‘‘If you overlay two different video images you may combine a red and a green pixel and get a messy purple pixel as a result, but if you do a pixel by pixel image fusion, where you take the best that one wavelength has to offer, perhaps visible for spatial resolution of facial detail and IR to see through a smoky area, then you have the power of image fusion,’‘ Francisco explains.
Throughout much of machine vision’s early years, thermal imaging has suffered from the return on investment (ROI) argument, but in today’s changing security environment, and given the drop in prices uncooled LWIR cameras, fiscal momentum has switched in favor of the IR vendor and customer. ‘‘What drives cost is not just the technology, but also volume and as we get into big volume commercial markets, like automotive manufacturing (L-3IP was the first company to put a NightDriver passive uncooled LWIR system on a commercially available automotive platform), costs will be driven down. Ten years ago, you couldn’t get an IR camera under $18,000. Now it’s under $10,000, and we can expect that trend to continue as new technologies come along,’‘ said Francisco.
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