Playing with markers today yielded some interesting results ~ I'm using a rather brute force approach in opencv by using InRangeS against an HSV image, and have pulled the ranges via an app to obtain a sample of representative pixels. Quick and dirty, but I'm encouraged by the results. False positives are high and its not robust in terms of working across lighting conditions but in-part the problem is the choice of material for the markers (electrical masking tape) which suggests :
CVFB-R5: Markers must be composed of a material which is minimally reflective.
I need to define "minimally" with more precision obviously but I think the above would improve the detection in varied lighting conditions and possibly reduce false positives if I resample. And yes, I know this should have been obvious but I have to harken back to my comments about a lack of design rationale...
I'd be reasonable confident that with a few improvements that this would roughly match the functionality of the SixthSense system so anything over and above this is an improvement.
Next steps are to look at cvblob and then move onto Neural Networks
Some notes : Popović wang ~ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.163.3388&rep=rep1&type=pdf ~ MIT data glove.
kakuman A survey of skin-color modeling and detection methods