Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects

We investigate the problem of learning the structure of an articulated object, i.e. its kinematic chain, from feature trajectories under affine projections. We demonstrate this possibility by proposing an algorithm which first segments the trajectories by local sampling and spectral clustering, then builds the kinematic chain as a minimum spanning tree of a graph constructed from the segmented motion subspaces. We test our method in challenging data sets and demonstrate the ability to automatically build the kinematic chain of an articulated object from feature trajectories. The algorithm also works when there are multiple articulated objects in the scene. Furthermore, we take into account non-rigid articulated parts that exist in human motions. We believe this advance will have impact on articulated object tracking and dynamical structure from motion.