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Two-person activity recognition using skeleton data

Manzi, Alessandro; Fiorini, Laura; Limosani, Raffaele; Dario, Paolo; Cavallo, Filippo (2017), Two-person activity recognition using skeleton data, IET Computer Vision, 12, 1, p. 27-35. 10.1049/iet-cvi.2017.0118

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Type
Article accepté pour publication ou publié
Date
2017
Journal name
IET Computer Vision
Volume
12
Number
1
Publisher
IEEE Xplore
Pages
27-35
Publication identifier
10.1049/iet-cvi.2017.0118
Metadata
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Author(s)
Manzi, Alessandro
BioRobotics Institute of Sant'Anna [Pisa]
Fiorini, Laura
BioRobotics Institute of Sant'Anna [Pisa]
Limosani, Raffaele
BioRobotics Institute of Sant'Anna [Pisa]
Dario, Paolo
BioRobotics Institute of Sant'Anna [Pisa]
Cavallo, Filippo
BioRobotics Institute of Sant'Anna [Pisa]
Abstract (EN)
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.
Subjects / Keywords
activity recognition; assisted living; machine learning; depth camera

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