RGB-D-based action recognition datasets: a survey

Authors

Jing Zhang, University of WollongongFollow
Wanqing Li, University of WollongongFollow
Philip O. Ogunbona, University of WollongongFollow
Pichao Wang, University of WollongongFollow
Chang Tang, Tianjin University, University of Wollongong

RIS ID

110140

Publication Details

Zhang, J., Li, W., Ogunbona, P. O., Wang, P. & Tang, C. (2016). RGB-D-based action recognition datasets: a survey. Pattern Recognition, 60 86-105.

Abstract

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against stateof-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-á-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.