Motion as shape: A novel method for the recognition and prediction of biological motion

We introduce a method for the recognition and prediction of motion, based on the idea that different motions trace out different shapes in some state space. In the recognition step we use a multidimensional generalization of the shape context [1] to find the closest prototype motion to the observed data. When tested against motion capture data, our model yields excellent (99%) recognition of gait and good (83%) recognition of identity. In addition to recognition, this process also allows us to find an aligning transform T _DP that maps the observed data D onto the prototype P. Given this transform, and its inverse T_PD, we use a Bayesian approach to make optimal predictions about the data in the prototype space and then map these predictions back into data space. This approach gives accurate predictions over several gait cycles despite the fact that there is often a significant difference between the observed data and the prototype manifold.