Software Detects Motion that the Human Eye Can't See
The video technique could lead to remote diagnostic methods, like the ability to detect the heart rate of someone on a screen.
A new set of software algorithms can amplify aspects of a video and reveal what is normally undetectable to human eyesight, making it possible to, for example, measure someone’s pulse by shooting a video of him and capturing the way blood is flowing across his face.
The software process, called “Eulerian video magnification” by the MIT computer scientists who developed the program, breaks apart the visual elements of every frame of a video and reconstructs them with the algorithm, which can amplify aspects of the video that are undetectable by the naked eye. These aspects could include the variations in redness in a man’s face caused by his pulse. “Just like optics has enabled [someone] to see things normally too small, computation can enable people to see things not visible to the naked eye,” says MIT computer scientist Fredo Durand, one of the coauthors of a paper about the research.
UC Berkeley professor Maneesh Agrawala, who has spent his career in visualization and computer graphics, says he is impressed with the work. “The many examples in the video they provide are really nice examples of visualizing things that are difficult to do otherwise,” he says.
Durand and his colleagues plan to make their software code available to others this summer. He predicts the primary application will be for remote medical diagnostics, but it could be used to detect any small motion, so that it might let, for example, structural engineers measure the way wind makes a building sway or deform slightly.
He adds that any video footage can be used, although depending on the quality of the camera that captured the footage, noise and artifacts such as graininess will also be amplified. So the higher quality the footage, the better the outcome using the program. “What’s really nice about this technique is that it can just take standard video, from just about any device, and then process it in a way that finds this hidden information in the signal,” Agrawala says.
Become an MIT Technology Review Insider for in-depth analysis and unparalleled perspective.Subscribe today