A multiclass ELM strategy in pose-based 3D human motion analysis

This paper pursues the best multiclass classification strategy for pose-based 3D human motion recognition using Extreme Learning Machines (ELM). Such classification task is one of the most difficult classification problem because the pose is not unique and the same pose might be randomly distributed inside any unrelated and absolutely different activities. In this study, bag of poses are selected as features and several multiclass classification strategies commonly used in binary classifiers such as Support Vector Machines (SVM) are adopted into the ELM implementation and then compared with non-binary classifications of ELM and binary classifications of SVM. A number of multiclass strategies such as One-Against-All (OAA), One-Against-One (OAO), Directed Acyclic Graph (DAG), hierarchical binary tree, and OAO-3Tree are evaluated and analysed. We found that the OAO-3Tree strategy using Max-Win vote fusion of labeled output function gives the best result.