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.
Lingua originale | English |
---|---|
pagine (da-a) | 130-147 |
Numero di pagine | 18 |
Rivista | Pattern Recognition |
Volume | 53 |
Stato di pubblicazione | Published - 2016 |
All Science Journal Classification (ASJC) codes
- ???subjectarea.asjc.1700.1712???
- ???subjectarea.asjc.1700.1711???
- ???subjectarea.asjc.1700.1707???
- ???subjectarea.asjc.1700.1702???