interaction error based viewpoint estimation for ... · user study – tracking setup ! tracking...

Interaction Error based Viewpoint Estimation for Continuous Parallax

Error Correction on Interactive Screens

Bastian Migge (ETH Zurich), Andreas Kunz (ETH Zurich), Tim Schmidt (PARC)

Content

n  Introduction/Motivation: Interactive surfaces n  Problem: Parallax error n  Calibration Techniques n  Model based viewpoint estimation n  Contribution: Empirical observation model for MPC

–  User study –  Discrete Observation Model

n  Conclusion and outlook

Bastian Migge © 06/2011 IWF, ETH Zurich 2

Interactive Surfaces

n  Pros –  Intuitive operation, easy to learn –  Innate interaction (input and output combined)

n  Requisition –  Good alignment between image plane and

tracking system is essential

3

Digital Desk (Crion table, ETH Zurich)

Single display groupware (Collaboard, ETH Zurich)

Ticketing machine (New York Underground)

Bastian Migge © 06/2011 IWF, ETH Zurich

Parallax Error

4

n  Vz : Distance between image and interaction plane

n  Vx : Resulting parallax distortion in x-dimension

n  ax,z: Distance from user to interaction point in x,z

z

xzx a

aVV ⋅=Parallax Error

Interaction plane and image pane with offset1 (analogous for y)

1 Source: Migge,Kunz: User Model for Predictive Calibration Control on Interactive Screens, IEEE CW 2010

Image plane

Interaction plane4

Vx

User A

23

1

ax

az

Vz

z

xy

User B

5


Static Calibration

n  Initially to correct geometric distortion n  A-priori setup n  Is biased by user characteristics

(height, arm length) n  Depends on a single viewpoint

n  Can not deal with user’s motion n  Can not handle multiple users n  Can not overcome the parallax error

stemming from changing viewpoints (VP)

5

Static Calibration Process


Continuous Calibration with Viewpoint Estimation

n  Method1: –  Remove sensors - Estimate users viewpoint –  Add filter – model the users position and movement –  Set the correction for the next (predicted position) interaction –  Consider effect of correction actions (user disturbance)

à  Model based Predictive Parallax Error Correction under Uncertainty (POMDP)

n  Pro –  No additional hardware

n  Cons –  Models needed (a priori) –  Interaction on screen needed (at runtime)

6 Bastian Migge © 06/2011 IWF, ETH Zurich

1[Migge, Kunz, Schmidt: POMDP Models for Continuous Calibration of Interactive Surfaces, AAAI SS 2010]

Model based Predictive Parallax Error Correction under Uncertainty

n  Parallax correction (-Vx): shift pointing device information n  Information sources

–  Target can be assumed to be next to Touch point –  Viewpoint is not directly observable –  VP can be inferred from GUI interactions (ß Vx, Vz)

n  Benefits –  Correction for all touch points –  Increases the pointing accuracy

7

Image plane

Interaction plane

Vx

User's viewpoint

Touch point

Target

xz

Vz


Contribution: Viewpoint estimation from interaction error

8

n  Contribution: A model of the user’s interaction behavior to estimate the viewpoint of the user

n  Static Characteristics: –  Display offset Iz

n  Dynamic Values: –  Relative User position VPx –  Relative Interaction Error Ix –  VPx ~ Ix

n  Observation Model: –  Pr(Ix = err |VPx = vp)

Image plane

Interaction plane

User

Target

VPzz

xy

InteractionIz

VPx

Ix

Dependency between relative viewpoint and relative pointing error


User Study – Measurement Setup

n  Participants: 13(4) (fe)male, avg. age 31.05, avg. height 179mm n  Interactive System

–  50“ interactive surface (plasma screen with SMART tracking) –  Pixel pitch 0.858 x 0.808 mm, Resolution 1280 x 768 px –  On-screen target: 15 x 15 px (13 x 12 mm) –  Parallax offset ≥ 13 mm

à Measures the Interaction error (in 2D display coordinates)

9

(b) Assembly of the tracking system on top of the display (a) Schematic measurement setup with test person

10

6.4

652.6

[mm]

Touch sensitive film

Glas plane

Displaybody

Pen

785

mm

1215

mm


User Study - Task

n  Random clicker –  User must click a single button of a full screen application –  Button moves after each interaction –  Python, QT, X11, Linux system

à Provides global interaction position on display


Test application

User Study – Tracking Setup

n  Tracking System: Qualisys Motion Tracking System –  4 Oqus 300 Camera –  IR based out-side in system –  Measures Position of reflective marker (passive) –  50 Hz; σ = 0.87 mm

à Measures the 3D Viewpoint and transforms 2D display coordinates to global 3D coordinate system

11

(b) Passive marker on interactive screen (a) Head tracking module 1http://www.qualisys.com

1


Vertical measurements

User Study – Results 1/2 Pointing Error

12

n  Pointing error n  Deviation from

assumed hit point (haptic error)

Boxplots Horizontal measurements

−40 −20 0 20 40 60

0.00

0.05

0.10

0.15

0.20

Error [mm]

Inte

ract

ions

[%]

Pointing ErrorDeviation from assumed hit

−40 −20 0 20 40

0.00

0.05

0.10

0.15

0.20

Error [mm]

Inte

ract

ions

[%]

Pointing ErrorDeviation from assumed hit

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−40

−20

0

20

40

Nor

mal

ized

devi

atio

nPointing error Deviation from assumed hit point

Image plane

Interaction plane4

User

z

xytarget

actual hit point

assumed viewpointassumed hit point

Deviation from assumed hit


Pointing error and Haptic error

User Study – Results 2/2 Correlation

13

n  Normalized viewpoint interaction error n  5500 interactions

Horizontal measurements Vertical measurements

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−0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6

−1.0

−0.5

0.0

0.5

Pearsons correlation coefficient ( 0.806 )

Viewpoint

Inte

ract

ion

erro

r

Least square fitLowess fit

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−0.6 −0.4 −0.2 0.0 0.2 0.4

−1.0

−0.5

0.0

0.5

Pearsons correlation coefficient ( 0.011 )

Viewpoint

Inte

ract

ion

erro

r

Least square fitLowess fit


Data interpretation: Discrete Observation Model

n  Measurement Data: Correlation between Interaction error and Viewpoint

n  Allows the controller to infer the viewpoint from the interaction error

n  Set up the Discrete Model –  Observation space O –  State space S –  P(O|S=s) as probability

distribution

14

inferred viewpoint

image plane

interaction plane

applied correction

actual viewpoint

ohit orightoleft

targetcenter

Observation (O) model for a given viewpoint and target (S) as discrete probability distribution P(O|S)


Discrete Observation Model Concrete Example

n  Continuous observation space, Discrete states –  Click error significantly differs for different viewpoints

n  Discrete observation space

15

Horizontal click error for 5 discrete viewpoint states

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[ −1 , −0.55 ) [ −0.15 , 0.15 ) [ 0.55 , 1 )

−1.0

−0.5

0.0

0.5

Viewpoint (State)

Clic

kerro

r (O

bser

vatio

n)

Observation model ( #states= 5 #observations= 5 )Relative click error

[ −0.15 , 0.15 ) mean −0.286 var 0.065

Den

sity

−2 −1 0 1 2

0.0

0.5

1.0

1.5

P(O|s=“left”)

Observation model ( #states= 5 #observations= 5 )Relative click error

[ 0.15 , 0.55 ) mean 0.202 var 0.036

Den

sity

−2 −1 0 1 2

0.0

0.5

1.0

1.5

P(O|s=“right”)


Conclusion and Outlook

n  Focus was to model the correlation between viewpoint position and interaction error on the screen –  Viewpoint can be distinguished

n  model the user’s behavior exists

n  Complete empirical model –  The correlation between pointing accuracy and target size –  Effect of correction actions onto the user

n  Develop prototype –  Prove of concept –  Compare controller to classical correction methods and

tracking based correction


Bastian Migge

[email protected]


Continuous Calibration with Viewpoint Tracking

n  Methods: –  Visual marker tracking or –  Video image feature extraction

n  Pro –  High quality viewpoint position

n  Cons –  Marker at tracked object not applicable –  Additional camera hardware –  Initial calibration pose needed –  Camera lens opening angle critical

18

3D Skelton Tracking with Microsoft Kinect and OpenNI

1http://luka.tnode.com/gallery/projects-and-work/kinect-hackery/skeleton-tracking


Model based Predictive Parallax Error Correction under Uncertainty – in detail

n  Control: Set the interaction correction parameter Vx, Vy

n  Uncertainty: Interaction error on screen (Observations) does indicate the viewpoint under uncertainty

n  Predictive: Model the User’s behavior predicting his movement 1

n  Method: Partial Observable Markow Decision Process (POMDP) –  User’s behavior modeled as time discrete Markow Chain

(state space) –  Add control à Markow Decision Process (MDP) –  Uncertain measurements indicate states (POMDP)

n  Needs a model of the user’s interaction behavior


1[Migge, Kunz :User Model for Predictive Calibration Control on Interactive Screens, IEEE CW 2010]

interaction error based viewpoint estimation for ... · user study – tracking setup ! tracking...

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