But the algorithm gets less accurate the more people it tries to track.
Scientists have trained drones to recognise violent behaviour in crowds using AI.
In a paper called Eye in the Sky, researchers from Cambridge University and India's technology and sciences institutes detailed how they fed an alogirthm videos of human poses to help their camera-fitted drones detect people committing violent acts.
The researchers claim the system boasts a 94% accuracy rate at identifying violent poses,and works in three steps: first the AI detects humans from aerial images, then it uses a system called "ScatterNet Hybrid Deep Learning" to interpret the pose of each detected human and finally the orientation of the limbs in the estimated pose are numbered and joined up like a coloured skeleton to identify individuals.
The algorithm used by the AI is trained to match five poses the researchers have deemed violent, which are classified as strangling, punching, kicking, shooting and stabbing.
Volunteers acted out the poses to train the AI, but they were generously spaced out and used exaggerated movements whilst acting out attacks. The report explains that the larger the crowd, and the more violent individuals within it, the less accurate the AI becomes.
"The accuracy of the Drone Surveillance System (DSS) decreases with the increase in the number of humans in the aerial image. This can be due to the inability of the FPN network to locate all the humans or the incapability of the SHDL network to estimate the pose of the humans accurately," the researchers wrote. "The incorrect pose can result in a wrong orientation vector which can lead the SVM to classify the activities incorrectly."
When one violent individual is in the crowd, the system is 94.1% accurate, which reduces to 90.6% with two, down to 88.3% for three, 87.8% for 4 and 84% for five violent individuals. By those figures, tracking violence in widespread incidents like the 2011 riots would currently be unworkable.