By creating a database of human motions, Yamane et al. hope to allow robots to recognize human behaviors or move like humans. To do this, they analyze motion clips of people performing all sorts of actions such as jumping, running and walking. Motion clips can be seen as a sequence of frames in which the body’s state is described by virtual markers that have a specific position and velocity as shown below. The challenge is then to break these clips down so that the important information can be stored and used in an intelligent manner.
The method used to create the database is described in the figure below. Starting from motion clips, they construct a binary tree. The root of this tree contains all frames in all clips. The root is then split into two groups where each group has similar features. Each one of these groups is then divided and so on until the tree is complete. Each layer of the tree contains all the frames in the dataset. Since for each frame it is known what frame follows (based on the clips), it is possible to compute the probability of transitioning from one node to the other (node transition graphs).
By using this database, Yamane et al. are able to recognize newly observed motion sequences, estimate the current state and predict future motions, and plan new human-like motions.