Page 8 - Security Today, November/December 2018

Watch for New Trends Expect to see additional complex algorithms and more accurate detection methods
By Brian Carle
What technology and industry trends should you keep an eye out for to stay ahead of the pack? In short, complex analytics are growing hot- ter which is made possible by the trend towards GPU co-processing. Also, the country of origin of security products is gaining more attention as tariffs and government regulations draw attention to the geographic source of equipment.
Facial Recognition
More complex analytics algorithms, such as facial recognition, require substantially more computational power than simple analytics, such as cross line detection. Until recently this attribute has made more advanced analytics and accurate detection methods impractical with available hardware platforms. With the advent and popularization of co-processing technologies, such as Graphics Processing Unit (GPU) co-processing, processing large amounts of video in a short time has recently become affordable and practical. As such, facial recognition is starting to become a reality, and facial recognition products are now penetrating the market even in commercial deployments.
Savvy consumers are incorporating advanced analytics such as facial recognition to make better use of massive amounts of video data. Facial recognition enables video surveillance to detect known persons of interest attempting to enter a secure facility; a task that’s nearly impossible for a person to accomplish. While a person can certainly look for another individual, the task becomes impractical when a long list of known persons of interest exists, and detecting all of them is important.
Each consumer segment has its own common targets for detec- tion. Some examples of current and potential facial recognition ap- plications include:
• Schools identifying parents without visitation rights • Retailers identifying known shoplifters
Generally, such deployments limit the number of cameras facial recognition is running on, in order to reduce costs. Both the facial recognition software and the corresponding hardware to process the video can be expensive. Maximizing the ROI potential involves re- ducing system costs by focusing the technology on areas where the facial recognition is most likely to be successful. This can be done by limiting the number of video channels being processed for facial recognition to just the ingress/egress cameras, or cameras in key loca- tions (like security lanes at airports). Doing so minimizes the corre- sponding hardware and licensing costs and maximizes the chances of getting alerts on known persons of interest.
Other applications involve public and private partnerships. For some time now, major sporting arenas hosting events have used facial recognition software to compare faces of fans in attendance against databases of wanted criminal suspects. Such a public/private partner- ship could also be deployed in hospitals for the same purpose.
Search Analytics
Search analytics are a category of analytics products which detect and classify objects in recorded video. During an investigation, large segments of video can be filtered more quickly by specified criteria, harvested from the metadata in the processed video.
Such a product may allow for an investigator to import a sample image of an object or person being looked for. Using the image sample, the analytics will search the corresponding recorded video for a match.
Alternatively, a search analytic may detect and classify all ob- jects in a range of recorded video to allow filtering by category, color and other attributes. For instance, an incident may have oc- curred on a range of cameras over the past 36 hours. The last 36 hours of video recorded from the cameras located close to the inci-
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Airports looking to detect terror suspects Casinos watching for known cheaters
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