Shadow Walking An Unencumbered Locomotion Technique for Systems - - PowerPoint PPT Presentation

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Shadow Walking An Unencumbered Locomotion Technique for Systems - - PowerPoint PPT Presentation

Shadow Walking An Unencumbered Locomotion Technique for Systems with Under-floor Projection David J. Zielinski, Ryan P. McMahan, Rachael B. Brady Motivation: Walking in Place An alternative to real-walking locomotion Overcomes the requirement


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Shadow Walking

An Unencumbered Locomotion Technique for Systems with Under-floor Projection David J. Zielinski, Ryan P. McMahan, Rachael B. Brady

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Motivation: Walking in Place

An alternative to real-walking locomotion Overcomes the requirement for a large physical space Uses same muscles as real walking (increased presence)

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Related Work

Slater, M., Usoh, M., and Steed, A., 1995. Analyze head position with a neural net Templeman et al, "Gaiter" 1999. Feasel, Whitton, and Wendt. 2008

Trackers on knees, sensors in insoles

Swapp, Williams, Steed. "Wizdish" 2010 User walks in a dish with low friction shoes Various device based systems Treadmills, Bicycles, ...

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Shadow Tracking

Track the shadows cast by the user's feet to calculate ground-plane location, orientation, and step events.

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Let's See the System in Action!

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System Setup

6-sided cube. 3m x 3m x 3m. PS3 Eye Webcam 640x480 @ 30 fps

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Simple Low Latency Image Processing

Blur (radius=2) Threshold (val<30) Original

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Calculating Position and Orientation

Center of Mass Furthest Point Orientation

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Step Determination

1-Foot State if Cluster2 / Cluster1 < 50% then state 1 2-Foot State if Cluster2 / Cluster1 > 75% then state 2

Cluster1 Cluster2

if state transitions, then send step event to the VE

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Extended Gestures

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First Impressions

Easy and natural to use Range of walking speeds Can we compare our technique to the IS-900 head tracker? Walking in place with turning. mean yaw error = 7.3° (σ = 5.0°) Walking around mean pos error = 13.4 cm (σ = 3.2 cm)

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Advantages

Unencumbered Low Cost: $40 Camera (+ $1M VR System) No per user calibration

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Limitations

Need to have a system that was built for underfloor projection Dependence on scene for illumination (our testing was done with an idealized scene) Need to walk with a flat footed step to detect orientation. No Droopy Pants

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Future Work - Robustness

Eliminate dependency on scene Low illumination Non-uniform floor illumination. Can we increase position and

  • rientation accuracy ?

User study Ideas: How well does our technique integrate into existing VR applications ? Does our technique work across a range of foot sizes?

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Future Work - Applications

Additional extended gestures Collaborative walking Artistic purposes: sound generation collaborate with dancers

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David J. Zielinski Software Engineer for the Visualization Technology Group

Authors

Rachael B. Brady Director of the Visualization Technology Group Ryan P. McMahan Ph.D. Candidate

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Thank You! Questions?

http://www.duke.edu/~djzielin/

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Bonus Slide: Algorithm For Travel

if only one step event in the queue completion_time = 0.5 else completion_time = step time stamp - next step time stamp speed = 0.5 / completion_time //in ft per second do travel if time spent on this step > completion_time pop step off the queue at each frame