  Human activity recognition has drawn considerable attention recently in the
field of computer vision due to the development of commodity depth cameras, by
which the human activity is represented as a sequence of 3D skeleton postures.
Assuming human body 3D joint locations of an activity lie on a manifold, the
problem of recognizing human activity is formulated as the computation of
activity manifold-manifold distance (AMMD). In this paper, we first design an
efficient division method to decompose a manifold into ordered continuous
maximal linear patches (CMLPs) that denote meaningful action snippets of the
action sequence. Then the CMLP is represented by its position (average value of
points) and the first principal component, which specify the major posture and
main evolving direction of an action snippet, respectively. Finally, we compute
the distance between CMLPs by taking both the posture and direction into
consideration. Based on these preparations, an intuitive distance measure that
preserves the local order of action snippets is proposed to compute AMMD. The
performance on two benchmark datasets demonstrates the effectiveness of the
proposed approach.
