After a quick liquid adjustment, and a coffee fix – we are back with the next session of ISMAR ’08, tackling a major topic in augmented reality: Tracking.
Youngmin Park is first on stage with Multiple 3D Object Tracking. His first demonstration is mind blowing. He shows an application that tracks multiple 3D objects, which have never been done before – and is quite essential for an AR application.
The approach combines the benefits of multiple approaches while avoiding their drawbacks:
- Match input image against only a subset of keyframes
- Track features lying on the visible objects over consecutive frames
- Two sets of matches are combined to estimate the object 3d poses by propagating errors
Conclusion: Multiple objects are tracked in interactive frame rate and is not affected by the number of objects.
Don’t miss the demo.
Next two talks with Daniel Wagner from Graz university about his favorite topic Robust and Unobtrusive Marker Tracking on Mobile Phones.
Why AR on cell phones? there are more than a billion phones out there and everyone knows how to use them (which is unusual for new hardware).
A key argument, Daniel is making: Marker tracking and natural feature tracking are complementary. But we need a more robust tracking for phones, and create less obtrusive markers.
The goal: Less obtrusive markers. Here are 3 new marker designs:
The frame markers (the frame provides the marker while the inner area is used to present human readable information.
The split marker (somewhat inspired by Sony’s by the eye of judgment) we use barcode split, with a similar thinking to the frame marker.
A third marker is a Dot marker. It covers only 1% of the overall area (assuming it’s uniquely textured – such as a map).
Incremental tracking using optical flow:
These requirements are driven from industrial needs: “more beautiful markers” and of course making them more robust.
Daniel continues with the next discussion about Natural feature tracking on mobile phones.
Compared with marker tracking, natural feature tracking is less robust, more knowledge about the scene, more memory, better cameras, more computational load…
To make things worse, mobile phones have less memory, with less processing power (and no floating point computation), and a low camera resolution…
The result is that a high end cell phone runs x10 slower than a PC, and it’s not going to improve soon, because the battery power is limiting the advancement of this capabilities.
So what to do?
We looked at two approaches:
- SIFT (one of the best object recognition engines – though slow) and –
- Ferns (state of the art for fast pose tracking – but is very memory intensive)
So both approaches wont work for cell phones…
The solution: combine the best of both worlds into what they call: PhonySift (Modified SIFT for phones). And then complementing it with PhonyFern – detecting dominant orientation and predicting where the feature will be in the next frame.
Conclusion: both approaches did eventually work on mobile phones in an acceptable fashion. The combined strength made it work, and now both Fern and Sift work at similar speeds and memory usages.
From ISMAR ’08 Program:
Multiple 3D Object Tracking for Augmented Reality
Youngmin Park, Vincent Lepetit, Woontack Woo
Robust and Unobtrusive Marker Tracking on Mobile Phones
Daniel Wagner, Tobias Langlotz, Dieter Schmalstieg
Pose Tracking from Natural Features on Mobile Phones
Daniel Wagner, Gerhard Reitmayr, Alessandro Mulloni, Tom Drummond, Dieter Schmalstieg
Filed under: AR Engines, AR Events | Tagged: Alessandro Mulloni, AR MArker Tracking, AR Tracking, Daniel Wagner, Dieter Schmalstieg, Gerhard Reitmayr, ISMAR 08, Pose Tracking, Tobias Langlotz, Tom Drummond, Vincent Lepetit, Woontack Woo, Youngmin Park |