Hierarchical Human Action Recognition with Self-Selection Classifiers via Skeleton Data

摘要

Human action recognition has become one of the most active research topics in human-computer interaction and artificial intelligence,and has attracted much attention.Here,we employ a low-cost optical sensor Kinect to capture the action information of the human skeleton.We then propose a two-level hierarchical human action recognition model with self-selection classifiers via skeleton data.Especially different optimal classifiers are selected by probability voting mechanism and 10 times 10-fold cross validation at different coarse grained levels.Extensive simulations on a well-known open dataset and results demonstrate that our proposed method is efficient in human action recognition,achieving 94.19％ the average recognition rate and 95.61％ the best rate.