Boston PD used facial recognition surveillance during 2013 music festival

During the 2013 Boston Calling music festivals, the Boston police department used a facial recognition surveillance system to keep an eye on those who attended. Thousands of faces were captured, according to Dig Boston, via ten cameras that could perform so-called "intelligent video analysis" in real time.

The story is an interesting one, something that revolves around Dig Boston's reporters "searching the deep web" and spotting unsecured documents related to the Boston Calling surveillance programs. IBM is said to have worked with law enforcement in providing a facial recognition system that would tag "every person" that attended.

IBM is said to have licensed an Intelligent Operations Center, and at the heart of it all was a system being tested via Boston Calling surveillance that analyzed, in real time, things like faces and bodies, skin color, clothing, traffic patterns, and more. In addition, information nabbed from social networks was integrated in real time and factored into the overall equation.

There is a division between what the Boston PD says about the discovery and what the alleged documents reveal. According to Dig Boston, the docs have photos of police officers watching the IBM system while the music festival took place, but a statement from the department said, "BPD was not part of this initiative. We do not and have not used or possess this type of technology."

Boston Mayor's press secretary had different things to say, however, confirming that surveillance was used during the two music festivals, summing it up by saying that ultimately the city didn't go with the software, because it had "not seen a clear use case for this software that held practical value for the City's public safety needs."



Source : slashgear.com

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Smart object-recognition system could spy on your milk in the IoT

Computers that can identify objects without requiring any human training are now a possibility, as researchers figure out how to teach AIs to intuit the key features and differences between faces, objects, and more. The new algorithm, developed by engineer Dah-Jye Lee of Brigham Young University, avoids human calibration by instead giving computers the skills to learn how to differentiate themselves: so, rather than the operator flagging individual differences between, say, a person and a tree, the computer is given the tools to identify the differences on its own, and then use them moving forward.

Lee says it's similar in theory to how a child is taught. His research saw a computer loaded with image sample sets - he picked faces, airplanes, cars, and motorbikes - and then the algorithm, known as "ECO features", left to calculate its own distinguishing elements.

For instance, the distinctive angles between the fuselage of a plane and its wings might be observed by the computer, which then allows it to tell the difference between that and a car. Lee's team found the algorithm was 100-percent accurate at recognizing each of the four datasets.

That high degree of accuracy continued when faced with the more difficult challenge of identifying objects within a category. Faced with classifying four different species of fish, Lee's algorithm achieved 99.4-percent success.

In contrast, rival object recognition systems only managed at most 98-percent success at distinguishing in the original four categories, never mind matching ECO features within categories.

Lee's team envisages ECO features being useful for unmanned and manpower-intensive applications like tracking invasive species in habitats and spotting flaws in products on production lines. Of course, there's also huge potential in the "Internet of Things" where systems are left to their own devices but could monitor individual people entering and leaving buildings, track what food you have left in your fridge, monitor individual cars as they navigate smart cities, and more.






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