Lumitrack

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Lumitrack is a motion capture technology developed by Robert Xiao, Chris Harrison and Scott Hudson at Carnegie Mellon University. It combines projectors and sensors to provide high-fidelity motion-tracking. These types of sensors are used in video game controllers, such as Microsoft's Kinect, and in motion capture for movie and television production.

Motion capture tracking procedure which makes it possible to detect any type of movement and convert it to a digital format

Motion capture is the process of recording the movement of objects or people. It is used in military, entertainment, sports, medical applications, and for validation of computer vision and robotics. In filmmaking and video game development, it refers to recording actions of human actors, and using that information to animate digital character models in 2D or 3D computer animation. When it includes face and fingers or captures subtle expressions, it is often referred to as performance capture. In many fields, motion capture is sometimes called motion tracking, but in filmmaking and games, motion tracking usually refers more to match moving.

Chris Harrison (computer scientist)

Chris Harrison is a British-born, American computer scientist and entrepreneur, working in the fields of human-computer interaction, machine learning and sensor-driven interactive systems. He is a professor at Carnegie Mellon University and director of the Future Interfaces Group within the Human-Computer Interaction Institute. He has previously conducted research at AT&T Labs, Microsoft Research, IBM Research and Disney Research. He is also the CTO and co-founder of Qeexo, a machine learning and interaction technology startup.

Scott Hudson (computer scientist) American computer scientist

Scott E. Hudson is a professor in the Human-Computer Interaction Institute at Carnegie Mellon University. He was previously an associate professor in the College of Computing at the Georgia Institute of Technology, and prior to that, an assistant professor of computer science at the University of Arizona. He earned his Ph.D. in computer science at the University of Colorado in 1986.

Contents

Although the research prototype of Lumitrack currently uses visible light, it could be adapted to utilize invisible infrared light. According to the university, the sensors require little power and should be cheap to mass-produce. They could even be built into smartphones.

Technology

The projectors cover the tracked area with structured patterns called a binary m-sequence that resemble barcodes. The series of bars encodes a series of an assortment of vertical lines of varying thicknesses, without repeating any combination of seven adjacent line types anywhere in the projected image. [1] The sensors read the bars to assess motion. The initial implementation offers sub-millimeter accuracy. When two m-sequences are projected at right angles to each other, the sensor can determine its position in two dimensions; while additional sensors enable 3D tracking. [2]

The sensors are simple to manufacture and require little power and features response times in the range of 2.5 milliseconds, [1] making them candidates for incorporation into other devices, such as phones. The sensors can be attached to tracked objects or to fixed objects such as walls. [2]

Applications

The developers target video games as an initial application. Other possibilities include including CGI for movies and television and human–robot interaction. [2]

Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences.

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References

  1. 1 2 "Super-precise motion tracking system uses projected "barcode" light patterns". Gizmag.com. Retrieved 2013-10-09.
  2. 1 2 3 "Carnegie Mellon-Disney motion tracking technology is extremely precise and inexpensive with minimal lag". KurzweilAI. Retrieved 2013-10-09.