Empirical estimation of tracking ranges
and application thereof for smooth transition
between two tracking devices
or
The MultiTracker service
Systementwicklungsprojekt
Sven Hennauer
Outline

Motivation

Requirements

System design

Object design

Transition strategies

Convex hull

Neural network
Sven Hennauer
The MultiTracker service - 2/15
The problem

Tracking is essential for AR

Trackers have limited working areas


ART: 4 x 4 metres

IS-600: 3 x 3 metres
Not sufficient for many applications
Sven Hennauer
The MultiTracker service - 3/15
The solution?

Use multiple trackers
Sven Hennauer
The MultiTracker service - 4/15
Tracking inaccuracies
ART
InterSense
Sven Hennauer
10cm
The MultiTracker service - 5/15
Requirements

Combine two trackers to increase tracking area

Assumption: Overlapping tracking areas

Challenges:


Smooth transition

Capable of learning
Embedded into DWARF

The MultiTracker service
Sven Hennauer
The MultiTracker service - 6/15
System design
Sven Hennauer
The MultiTracker service - 7/15
Object design
Sven Hennauer
The MultiTracker service - 8/15
Convex hull transition strategy

Estimate tracking areas out of tracking data

Assumption: Tracking areas are convex

Represent tracking areas as convex hulls

Learning phase:

Collect tracking data

Compute convex hull for each tracker (incrementally)
Sven Hennauer
The MultiTracker service - 9/15
Convex hull strategy – Application phase

Mix trackers based on
the distance to the
tracking boundary

Fade out ART

Fade in InterSense

Smooth transition
Sven Hennauer
The MultiTracker service - 10/15
Convex hull strategy – Results (1)

Strengths:

It works!

Efficient (even for online learning)
Sven Hennauer
The MultiTracker service - 11/15
Convex hull strategy – Results (1)

Strengths:

It works!

Efficient (even for online learning)
Sven Hennauer
The MultiTracker service - 12/15
Convex hull strategy – Results (2)

Weakness:

Outlier sensitivity
Sven Hennauer
The MultiTracker service - 13/15
Neural network transition strategy

Goal: No outlier sensitivity

Classification of the tracking data
Sven Hennauer
The MultiTracker service - 14/15
Conclusion

Convex hull strategy:


Neural network strategy:


Works, but suffers from outlier sensitivity
Doesn‘t work as expected
Future work:

Outlier detection for convex hull strategy

Combination of convex hull and neural net strategy?
Sven Hennauer
The MultiTracker service - 15/15