3D skeleton-based human action classification: A survey. (May 2016) - Lo Presti, Liliana; La Cascia, Marco
Abstract: In recent years, there has been a proliferation of works on human action classification from depth sequences. These works generally present methods and/or feature representations for the classification of actions from sequences of 3D locations of human body joints and/or other sources of data, such as depth maps and RGB videos. This survey highlights motivations and challenges of this very recent research area by presenting technologies and approaches for 3D skeleton-based action classification. The work focuses on aspects such as data pre-processing, publicly available benchmarks and commonly used accuracy measurements. Furthermore, this survey introduces a categorization of the most recent works in 3D skeleton-based action classification according to the adopted feature representation. This paper aims at being a starting point for practitioners who wish to approach the study of 3D action classification and gather insights on the main challenges to solve in this emerging field. Abstract : Highlights: State of the art 3D skeleton-based action classification methods are reviewed. Methods are categorized based on the adopted feature representation. Motivations and challenges for skeleton-based action recognition are highlighted. Data pre-processing, public benchmarks and validation protocols are discussed. Comparison of renowned methods, open problems and future work are presented.