Key-Skeleton-Pattern Mining on 3D Skeletons Represented by Lie Group for Action Recognition

The human skeleton can be considered as a tree system of rigid bodies connected by bone joints. In recent researches, substantial progress has been made in both theories and experiments on skeleton-based action recognition. However, it is challenging to accurately represent the skeleton and precisely eliminate noisy skeletons from the action sequence. This paper proposes a novel skeletal representation, which is composed of two subfeatures to recognize human action: static features and dynamic features. First, to avoid scale variations from subject to subject, the orientations of the rigid bodies in a skeleton are employed to capture the scale-invariant spatial information of the skeleton. The static feature of the skeleton is defined as a combination of the orientations. Unlike previous orientation-based representations, the orientation of a rigid body in the skeleton is defined as the rotations between the rigid body and the coordinate axes in three-dimensional space. Each rotation is mapped to the special orthogonal group . Next, the rigid-body motions between the skeleton and its previous skeletons are utilized to capture the temporal information of the skeleton. The dynamic feature of the skeleton is defined as a combination of the motions. Similarly, the motions are represented as points in the special Euclidean group . Therefore, the proposed skeleton representation lies in the Lie group , , which is a manifold. Using the proposed representation, an action can be considered as a series of points in this Lie group. Then, to recognize human action more accurately, a new pattern-growth algorithm named MinP-PrefixSpan is proposed to mine the key-skeleton-patterns from the training dataset. Because the algorithm reduces the number of new patterns in each growth step, it is more efficient than the PrefixSpan algorithm. Finally, the key-skeleton-patterns are used to discover the most informative skeleton sequences of each action (skeleton sequence). Our approach achieves accuracies of 94.70%, 98.87%, and 95.01% on three action datasets, outperforming other relative action recognition approaches, including LieNet, Lie group, Grassmann manifold, and Graph-based model.