Stanford University researchers have developed a computer-vision algorithm that predicts the movement of people in a busy space.
The researchers trained the deep-learning neural network using several publicly available datasets containing video of people moving around crowded areas.
The Stanford team, led by researcher Silvio Savarese, found the software is better at predicting peoples' movements than existing approaches for several of those datasets.
The researchers now are testing the algorithm on JackRobbot, a two-wheeled mobile robot equipped with cameras, range sensors, and a global-positioning system.
The Stanford algorithm adds to other research focused on making robot behavior more human-like.
"Much research in human-robot interaction has looked at whether we can replicate the norms of human social interaction," says Carnegie Mellon University professor Jodi Forlizzi.
Her own research has involved trying to get robots to move around spaces in such a way that they form natural-seeming clusters with people. "There's a whole class of robots that will be working with people and close to people also, so we need to understand how they should behave," Forlizzi says.
From Technology Review
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