With all of the progress we’ve seen in deep learning tech in the past few years, it seems pretty inevitable that security cameras become smarter and more capable in regards to tracking, but there are more options than we think in how we choose to pull this off.
Traces AI is a new computer vision startup, in Y Combinator’s latest batch of bets, that’s focused on helping cameras track people without relying on facial recognition data, something the founders believe is too invasive of the public’s privacy. The startup’s technology actually blurs out all human faces in frame, only relying on the other physical attributes of a person.
“It’s a combination of different parameters from the visuals. We can use your hair style, whether you have a backpack, your type of shoes and the combination of your clothing,” co-founder Veronika Yurchuk tells TechCrunch.
Tech like this obviously doesn’t scale too well for a multi-day city-wide manhunt, and leaves room for some Jason Bourne-esque criminals to turn their jackets inside out and toss on a baseball cap to evade detection. As a potential customer, why forego a sophisticated technology just to stave off dystopia? Well, Traces AI isn’t so convinced that facial recognition tech is always the best solution; they believe that facial tracking isn’t something every customer wants or needs and there should be more variety in terms of solutions.
“The biggest concern [detractors] have is, ‘Okay, you want to ban the technology that is actually protecting people today, and will be protecting this country tomorrow?’ And, that’s hard to argue with, but what we are actually trying to do is propose an alternative that will be very effective but less invasive of privacy,” co-founder Kostya Shysh tells me.
Earlier this year, San Francisco banned government agencies from the use of facial recognition software, and it’s unlikely that they will be the only city to make that choice. In our conversation, Shysh also highlighted some of the backlash to Detroit’s Project Green Light, which brought facial recognition surveillance tech city-wide.
Traces AI’s solution can also be a better option for closed venues that have limited data on the people on their premises in the first place. One use case Shysh highlighted was being able to find a lost child in an amusement park with just a little data.
“You can actually give them a verbal description, so if you say, ‘it’s a missing 10-year-old boy, and he had blue shorts and a white t shirt,’ that will be enough information for us to start a search,” Shysh says.
In addition to being a better way to promote privacy, Shysh also sees the technology as a more effective way to reduce the racial bias of these computer vision systems that have proven less adept at distinguishing non-white faces, and are thus often more prone to false positives.
“The way our technology works, we actually blur faces of the people before sending it to the cloud. We’re doing it intentionally as one of the safety mechanisms to protect from racial and gender biases as well,” Shysh says.
The co-founders say that the U.S. and Great Britain are likely going to be their biggest markets due to the high quantity of CCTV cameras, but they’re also pursuing customers in Asian countries like Japan and Singapore, where face-obscuring facial masks are often worn and can leave facial tracking software much less effective.