Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches
This work addresses activity recognition for computer vision applications, but it is incremental as it builds on existing manifold-based approaches with specific improvements.
The paper tackles human activity recognition from 3D skeleton data by formulating it as a manifold-manifold distance problem, introducing a method that decomposes manifolds into ordered linear patches and computes distances while preserving local order, achieving effective performance on benchmark datasets.
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.