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Kinect's AI Breakthrough Explained
mikejuk writes "Microsoft Research has just published a scientific paper (PDF) and a video showing how the Kinect body tracking algorithm works — it's almost as impressive as some of the uses the Kinect has been put to. This article summarizes how Kinect does it. Quoting: '... What the team did next was to train a type of classifier called a decision forest, i.e. a collection of decision trees. Each tree was trained on a set of features on depth images that were pre-labeled with the target body parts. That is, the decision trees were modified until they gave the correct classification for a particular body part across the test set of images. Training just three trees using 1 million test images took about a day using a 1000-core cluster.'"
Layered classification nets have always struck me as the right approach, particularly as we learn more about how human senses work - it seems like a lot of our "thinking" is done much closer to our sense organce than we might have once imagined. Interesting that the less "organic" type, decision trees, were used rather than neural nets. One wonders if maybe it was more a matter of ease of phrasing/training/debugging than of classification itself that decided which type to use.