Motion Tracking: No Silver Bullet, but a Respectable Arsenal

By G. Welch, E. Foxlin. IEEE Computer Graphics and Applications, 2002

630 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/261126e426a988106f6e66744730faa145b4ae78

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

3D Social Saliency from Head-mounted Cameras (2012, methodology): “For head orientation estimation, there are two approaches: outside-in and inside-out [23].”
State of the art of virtual reality technology (2016, background): “Welch and Foxlin [15] give an overview on existing tracking technologies describing and comparing their advantages and disadvantages.”
Augmented reality applications in manufacturing: a survey (2008, background): “Welch and Foxlin (2002) provided a comprehensive survey of motion tracking.”
Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing (2006, background): “…of their products, which would also include environment and motion trajectories, inertial sensing for orientation determination does not have a competitive edge over digital optical systems, when the latter technology is not subject to, e.g., line-of-sight or working volume limitations [31].”
Practical motion capture in everyday surroundings (2007, background): “The advantages and disadvantages of the dominant approaches are argued in several excellent surveys [Meyer et al. 1992; Frey 1996; Hightower and Borriello 2001; Welch and Foxlin 2002].”
3D Gestural Interaction: The State of the Field (2013, background): “There are several tracking technologies that support the attachment of an input device to the user’s hand including electromagnetic, inertial/acoustic, ultrasonic, and others [12].”
Pedestrian localisation for indoor environments (2008, background): “In a summary of such technologies, Welch and Foxlin concluded that a single technology that performs well in all situations has yet to be discovered [17].”
Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers (2010, background): “from delivering accurate position/orientation data beyond few seconds or minutes, unless a very sophisticated and complex filtering is applied to raw sensor signals [16].”
Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing (2011, background): “Several technologies and approaches are available to produce motion tracking systems (trackers), which derive orientation estimates from electrical measurements of acoustic, inertial, magnetic, mechanical, optical and radio frequency sensors [1].”