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Mostly AR and Stuff

Why 3d markerless tracking is difficult for mobile augmented reality

I often hear sentiments from users that they don’t like markers, and they are wondering, why there are so relatively few markerless AR around. First I want to say that there is no excuse for using markers in the static scene with immobile camera, or if desktop computer is used. Brute force methods for tracking like bundle adjustment and fundamental matrix are well developed and used for years and years in the computer vision and photogrammetry. However those methods in their original form could hardly produce acceptable frame rate on the mobile devices. From the other hand marker trackers on mobile devices could be made fast, stable and robust.
So why markers are easy and markerless are not ?
The problem is the structure , or “shape” of the points cloud generated by feature detector of the markerless tracker. The problem with structure is that depth coordinate of the points is not easily calculated. That is even more difficult because camera frame taken from mobile device have narrow baseline – frames taken form position close one to another, so “stereo” depth perception is quite rough. It is called structure from motion problem.
In the case of the marker tracker all feature points of the markers are on the same plane, and that allow to calculate position of the camera (up to constant scale factor) from the single frame. Essentially, if all the points produced by detector are on the same plane, like for example from the pictures lying on the table, the problem of structure from motion goes away. Planar cloud of point is essentially the same as the set of markers – for example any four points could be considered as marker and the same algorithm could apply. Structure from motion problem is why there is no easy step from “planar only” tracker to real 3d markerless tracker.
However not everything is so bad for mobile markerless tracker. If tracking environment is indoor, or cityscape there is a lot of rectangles, parallel lines and other planar structures around. Those could be used as initial approximation for one the of structure from motion algorithm, or/and as substitutes for markers.
Another approach of cause is to find some variation of structure from motion method which is fast and works for mobile. Some variation of bundle adjustment algorithm looks most promising to me.
PS PTAM tracker, which is ported to iPhone, use yet another approach – instead of using bundle adjustment for each frame, bundle adjustment is running in the separate thread asynchronously, and more simple method used for frame to frame tracking.
PPS And the last thing, from 2011:

30, March, 2009 Posted by | Coding AR | , , , , , , , , | 4 Comments

Tracking planes in the city

In relation to tracking cityscape I did some planar segmentation test. Segmented FAST generated corners with simple 5-points projective invariant.
In some cases 5-point give some rough approximation:
planar segments
In some cases outliers are quite bad – some point have very close projective invariant but still are in diffferent planes.
bad seggment
So simple method not quite work…

19, March, 2009 Posted by | Coding AR, computer vision | , , , , , , , , , | 4 Comments

Tracking cityscape

One of the big problem in image registration/structure from motion/3d tracking is using global information of the image. Feature/blob extraction, like SIFT, SURF or FAST etc using only local information around the point. Region detector like MSER using area information, but MSER is not good at tracking textures, and not quite stable at complex scenes. Edge detection provide some non-local information, but require processing edges. That could be computationally heavy, but looks promising anyway. There are a lot of methods which use global information – all kind of texture segmentation, epitome, snakes/appearance models, but those are computationally heavy and not suitable for mobiles. The question is how to incorporate global information from the image into tracker, and make it with minimal amount of operations. One way is to optimise tracker for specific environment – for example use the property of cityscape, a lot of planar structures and straight lines. Such multiplanar tracker wouldn’t work in the forest or park, but could be a working compromise.

12, March, 2009 Posted by | Coding AR | , , , , , , , , , , , , | Comments Off on Tracking cityscape

Region Tracking

Experimenting with MSER region tracking
The problem is that regions seems not stable enough and way too big.
MSER of downsampled images, original images, MSER of mean shift filtered images, MSER of smoothed images:
mser
Mean shift filtering seems capture more feauters, but it’s too computationally expensive.

3, March, 2009 Posted by | Coding AR | , , , , , , | Comments Off on Region Tracking

Markerless tracking with FAST

Testing outdoor markerless tracking with FAST/SURF feature detector.
The plane of the camera is not parallel to the earth, that make difficult for eye to estimate precision.
registration

29, January, 2009 Posted by | Uncategorized | , , , , , , , , | Comments Off on Markerless tracking with FAST

FAST with SURF descriptor

Feature detected with multistage FAST and fitted with SURF descriptors
FAST SURF
Less strict threshold give a lot more correspondences, but also some false positives
FAST SURF

25, January, 2009 Posted by | Coding AR | , , , , , , | 44 Comments

Multiscale FAST detector

Experimenting with multiscale FAST detector with images from cell phone camera.
FAST
so far so good…

24, January, 2009 Posted by | Coding AR | , , , , , , | 2 Comments

Testin FAST feature detector

Testing FAST feature detector on the Mikolajczyk ’s dataset. Here scale space seems actually useful. With “brick wall” dataset repeatability goes form .3 to .7 with scale from 0 t 2^^3, and threshold/barrier lowering from 40 to 20.

11, January, 2009 Posted by | Coding AR | , , , , , | Comments Off on Testin FAST feature detector

From SURF to FAST

I did some research on the SURF optimization. While it still possible to make it significantly faster with lazy evaluation, the problem of the scale remain. Fine-scale features are not detectedable on the bigger scale, so it doesn’t look like there is an easy way to reduce search area using only upper scale. If scale-space is not helping to reduce search area it become liability for mobile tracking – range can’t change too fast for a mobile pone, so scale of the feature will be about the same between frames.
Will try plain, not scale-space corner detectors now, starting with FAST.

5, January, 2009 Posted by | Augmented Reality, Coding AR | , , , , , , | Comments Off on From SURF to FAST

SURF scale scpace

I continue to test SURF, in respect to scale space. Scale space is essentially a pyramid of progressively more blurred or lower resolution images. The idea of scale invariant feature detection is that the “real” feature should be present at several scales – that is should be clearly detectable at several image resolution/blur levels. The interesting thing I see is, that for SURF, at least for test images from Mikolajczyk ‘s dataset, scale space seems doesn’t affect detection rate with viewpoint change. I meant that there is no difference if feature distinct in several scales or only in one. That’s actually reasonable – scale space obviously benefit detection in the blurred images, or noisy images, or repeatability/correspondence in scaled images , and “viewpoint” images form Mikolajczyk ‘s dataset are clear, high resolution and about the same scale. Nevertheless there is some possibility for optimization here.

23, December, 2008 Posted by | Coding AR | , , , , , , | Comments Off on SURF scale scpace