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. student, used their hands to generate thousands of views. — and then associates those parts with particular individuals.This new method was developed with the help of the Panoptic Studio, a two-story dome embedded with 500 video cameras.”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. It now is being used to improve body, face and hand detectors by jointly training them.To encourage more research and applications, the researchers have released their computer code for both multiperson and hand-pose estimation. The methods can be used for live events or applied to existing videos. student in robotics.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. “But computers are more or less blind to it. It already is being widely used by research groups, and more than 20 commercial groups, including automotive companies, have expressed interest in licensing the technology, Sheikh said.The challenges for hand detection are even greater. Sheikh and his colleagues took a bottom-up approach, which first localizes all the body parts in a scene — arms, legs, faces, etc.”In addition to Sheikh, the multiperson pose estimation research included Simon and master’s degree students Zhe Cao and Shih-En Wei.But for every image that shows only part of the hand, there often exists another image from a different angle with a full or complementary view of the hand, said Hanbyul Joo, a Ph. “We’re sharing the code, but we’re also sharing all the data captured in the Panoptic Studio.
This real-time detector sees hand poses and tracks multiple people.”Now, we’re able to break through a number of technical barriers primarily as a result of that NSF grant 10 years ago,” he added. “Hands are too small to be annotated by most of our cameras, however, so for this study we used just 31 high-definition cameras, but still were able to build a massive data set.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. The hand-detection study included Sheikh, Joo, Simon and Iain Matthews, an adjunct faculty member in the Robotics Institute.”The Panoptic Studio supercharges our research,” Sheikh said.”A single shot gives you 500 views of a person’s hand, plus it automatically annotates the hand position,” Joo explained. Unlike the face and body, large datasets do not exist of hand images that have been laboriously annotated with labels of parts and positions. Simply using programs that track the pose of an individual does not work well when applied to each individual in a group, particularly when that group gets large.”Joo and Tomas Simon, another Ph. The ability to recognize hand poses, for instance, will make it possible for people to interact with computers China metric nuts Suppliers in new and more natural ways, such as communicating with computers simply by pointing at things
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