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

الملخص EN

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 SO(3).

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 SE(3).

Therefore, the proposed skeleton representation lies in the Lie group (SE(3)×⋯×SE(3), SO(3)×⋯×SO(3)), 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.