Computer that Reads Body Language
TEHRAN (Tasnim) - Researchers at Carnegie Mellon University's Robotics Institute have enabled a computer to understand the body poses and movements of multiple people from video in real time -- including, for the first time, the pose of each individual's fingers.
This new method was developed with the help of the Panoptic Studio, a two-story dome embedded with 500 video cameras. The insights gained from experiments in that facility now make it possible to detect the pose of a group of people using a single camera and a laptop computer.
Yaser Sheikh, associate professor of robotics, said these methods for tracking 2-D human form and motion open up new ways for people and machines to interact with each other, and for people to use machines to better understand the world around them. The ability to recognize hand poses, for instance, will make it possible for people to interact with computers in new and more natural ways, such as communicating with computers simply by pointing at things.
Detecting the nuances of nonverbal communication between individuals will allow robots to serve in social spaces, allowing robots to perceive what people around them are doing, what moods they are in and whether they can be interrupted.
A self-driving car could get an early warning that a pedestrian is about to step into the street by monitoring body language.
Enabling machines to understand human behavior also could enable new approaches to behavioral diagnosis and rehabilitation for conditions such as autism, dyslexia and depression.
"We communicate almost as much with the movement of our bodies as we do with our voice," Sheikh said. "But computers are more or less blind to it."
In sports analytics, real-time pose detection will make it possible for computers not only to track the position of each player on the field of play, as is now the case, but to also know what players are doing with their arms, legs and heads at each point in time. The methods can be used for live events or applied to existing videos.
Sheikh and his colleagues will present reports on their multiperson and hand-pose detection methods at CVPR 2017, the Computer Vision and Pattern Recognition Conference, July 21-26 in Honolulu.