Computer vision is alerting authorities the moment someone draws a gun.
What’s new: Several companies offer deep learning systems that enable surveillance cameras to spot firearms and quickly notify security guards or police, according to Vice.
No people were harmed in the training of this model: Some developers of gun detection models have gone to great lengths to produce training data.
- Virginia-based Omnilert trained its Gun Detect system using simulations from video game software, scenes from action movies, and thousands of hours of video depicting employees holding toy or real guns.
- Alabama-headquartered Arcarithm, which makes systems for gun detection, produced training data by photographing guns in front of a green screen and compositing them into scenes such as offices. The company created 30,000 to 50,000 images of each of America’s 10 most popular rifles and handguns to train its Exigent-GR software.
- Other companies including Actuate, Defendry, Scylla, and ZeroEyes offer similar systems.
Behind the news: The use of computer vision in such offerings updates earlier systems based on sounds. For instance, ShotSpotter is used by over 100 police departments in the U.S. The system picks up gunshot sounds from acoustic sensors placed around a community and uses machine learning to compare them with an audio database. When it recognizes a gunshot, it triangulates the location and alerts police.
Why it matters: Gun violence is endemic across the U.S, including hundreds of mass shootings. By warning police or security guards before a shooter opens up, AI-powered gun detection could save lives.
We’re thinking: Like any machine learning system applied to the real world, gun detection algorithms aren’t perfect. One such system used in New York state schools was found to mistake broom handles for guns. Such mistakes could be dangerous if they prompt police to enter possible crime scenes with their own weapons drawn and pulses pounding.