Generic enhanced ensemble learning with multi-level kinematic constraints for 3D action recognition

The 3D human body skeleton conveys rich information of human action and is regarded as an important data modality for action recognition. Due to the diversity of human action and the noise in skeleton data, skeleton-based action recognition methods face the challenges of overcoming the interference of irrelevant data and learning enough valid information of human action. Previous research has led us to a variety of effective skeleton features and many deep network models with strong learning abilities. However, a single model using a single feature cannot make full use of the valid information in the skeleton. To address this problem, this paper proposes the multi-level kinematic constraints to construct multiple skeleton features. By using different levels of constraints, a set of features containing information from local to global are extracted. The variability among these features leads to significant variability in classifiers trained on them, thus enhancing the ensemble performance of these classifiers. Extensive experiments on three representative datasets and four kinds of classification models demonstrate the generality of the proposed method. A substantial improvement can be achieved on multiple kinds of existing well-performing models and our method surpasses most state-of-the-art skeleton-based action recognition methods.

Automated summary

This paper proposes a multi-level kinematic constraints method to construct multiple skeleton features, which can effectively utilize valid information and enhance the performance of skeleton-based action recognition methods.