RGB-D-based Action Recognition Datasets: A Survey

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 state-of-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-\'{a}-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

Recognition of Human Actions using Edit Distance on Aclet Strings

In this paper we propose a novel method for human action recognition based on string edit distance. A two layer representation is introduced in order to exploit the temporal sequence of the events: a first representation layer is obtained by using a feature vector obtained from depth images. Then, each action is represented as a sequence of symbols, where each symbol corresponding to an elementary action (aclet) is obtained according to a dictionary previously defined during the learning phase. The similarity between two actions is finally computed in terms of string edit distance, which allows the system to deal with actions showing different length as well as different temporal scales. The experimentation has been carried out on two widely adopted datasets, namely the MIVIA and the MHAD datasets, and the obtained results, compared with state of the art approaches, confirm the effectiveness of the proposed method