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      Two-person activity recognition using skeleton data

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          Abstract

          Human activity recognition is an important and active field of research having a wide range of applications in numerous fields including ambient-assisted living (AL). Although most of the researches are focused on the single user, the ability to recognise two-person interactions is perhaps more important for its social implications. This study presents a two-person activity recognition system that uses skeleton data extracted from a depth camera. The human actions are encoded using a set of a few basic postures obtained with an unsupervised clustering approach. Multiclass support vector machines are used to build models on the training set, whereas the X-means algorithm is employed to dynamically find the optimal number of clusters for each sample during the classification phase. The system is evaluated on the Institute of Systems and Robotics (ISR) - University of Lincoln (UoL) and Stony Brook University (SBU) datasets, reaching overall accuracies of 0.87 and 0.88, respectively. Although the results show that the performances of the system are comparable with the state of the art, recognition improvements are obtained with the activities related to health-care environments, showing promise for applications in the AL realm.

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          Most cited references 27

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          Instance-based learning algorithms

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            Real-time human pose recognition in parts from single depth images

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              Learning human activities and object affordances from RGB-D videos

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                Author and article information

                Contributors
                Journal
                IET-CVI
                IET Computer Vision
                IET Comput. Vis.
                The Institution of Engineering and Technology
                1751-9632
                1751-9640
                8 September 2017
                20 October 2017
                February 2018
                : 12
                : 1
                : 27-35
                Affiliations
                The BioRobotics Institute, Scuola Superiore Sant'Anna , Viale Rinaldo Piaggio, 34, 56026 Pontedera (PI), Italy
                Article
                IET-CVI.2017.0118 CVI.SI.2017.0118.R1
                10.1049/iet-cvi.2017.0118

                This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/)

                Page count
                Pages: 0
                Product
                Funding
                Funded by: ACCRA
                Award ID: European Community’s Horizon 2020 Programme (H20
                Funded by: CENTAURO
                Award ID: Regione Toscana, Call FAR-FAS 2014
                Categories
                Special Section: Computer Vision in Healthcare and Assisted Living

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