I was somewhat surprised to come across a patent for Sixth Sense given the initial declaration that the code would be open sourced. I was even more surprised reading the contents of the patent at how general it is...oh hum lets not go there apart from to say I'm not a fan of broad patents, but I wanted to bring it up since 1) it is the only "detailed" source of information on the implementation of Sixth Sense and 2) its useful to acknowledge and recognised since I don't particularly want to be "trolled" in this research.
So yes, its out there and its worth a quick skim through (or not since a "clean room" implementation might be advisable but too late for me!) since it tells us that the source for Sixth Sense is largely based on several open source projects (see 0120). Touchless is used for fiducial recognition, the $1 Unistroke Recogniser algorithm for gesture commands, and ARToolkit for the output. OpenCV is mentioned and possibly does some of the heavy lifting for object recognition (possibly HMM?). I also just realised that the microphone is working in tandem with the camera when it used on paper, probably using some aspect of the sound on the paper to indicate contact with the destination surface since a single camera by itself is insufficient to determine when contact occurs.
So what do we do with this knowledge?
I've played with OpenCV and HandVu in the past and found them (for hand tracking at least) not that great since neither really solve the problem of reliable background segmentation in complex environments hence I can see the logic in using fiducials although a brief play (with touchless) suggests that even a fiducial based recognition system is unlikely to be perfect (at least in the case of a single unmodified webcam). This does lead to an important point for me in terms of requirements :
CVFB-R1: The computer vision system must be able to reliably determine fiducial positions in complex background images.
CVFB-R2: The computer vision system must be able to reliably determine fiducial positions in varied background images.
CVFB-R3: The computer vision system must be able to reliably determine fiducial positions with varying lighting conditions.
CVFB-R4: CVFB-R1 - CVFB-R3 must be met for 4 fiducial markers, each of a distinct colour.
and should it be possible to work without fiducial markers :
CVSB-R1: The computer vision system must be able to reliably determine hand shape in complex background images.
CVSB-R2: The computer vision system must be able to reliably determine hand shape in varied background images.
CVSB-R3: The computer vision system must be able to reliably determine hand shape with varying lighting conditions.
CVSB-R4: The computer vision system must be able to reliably discriminate between left and right hands.
I rather suspect that I'm going to have to be flexible with tests/thresholds to determine if these requirements are met and it should also be noted that it has been recognised that no single based computer vision technique has been found to work for all applications or environments (Wach et al, 2011, p60) hence there may be some opportunity to improve on the generic libraries/algorithms which it would seem natural to apply (e.g. touchless, cvBlob)
Moving on, for those who haven't played with $1 Unistroke recogniser (Wobbrock, 2007) its impressive. I'd be reasonably confident based of the results of the tests for this algorithm in its reliability and robustness, IF the above requirements can be met.
Keeping to the KISS principle I'm going to use this as the basis of my first experiments (and code woo-hoo!) which are going to be :
1) Capture short (<5 minute) segments of video with a worn webcam (in my case I have a Logitech C910 handy, not the most discrete of cameras but sadly my Microsoft Life show broke grrrrr) in a variety of environments while wearing fiducial markers on 4 fingers.
2) Capture short (<5 minute) segments of video with a worn webcam in a variety of environments without markers.
3) Based on these exemplary videos test various recognition techniques from openCV to determine the optimal technique which meets the above requirements.
4) Apply and test sample gestures against $1 Unistroke Recogniser (Python implementation)
4.1) optional Determine if there are any differences in the performance/reliability of the Python versions.
Okay that's my week planned then, comments?
Wachs, J, Kölsch, M, Stern, H & EDAN, Y 2011, ‘Vision-based hand-gesture applications’ in Communications of the ACM vol. 54, no. 2 p. 60-71
Wobbrock et al 2007 http://depts.washington.edu/aimgroup/proj/dollar/