Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Claudio Coppola, Diego R. Faria, Urbano Nunes, Nicola Bellotto

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals; (2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers; and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies.

Original languageEnglish
Title of host publicationIROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
Pages5055-5061
Number of pages7
ISBN (Electronic)978-1-5090-3762-9
DOIs
Publication statusPublished - 1 Dec 2016
Event2016 IEEE/RSJ International Conference on Intelligent Robots and Systems - Daejeon Convention Center, Daejeon, Korea, Republic of
Duration: 9 Oct 201614 Oct 2016

Publication series

NameIEEE International Conference on Intelligent Robots and Systems. Proceedings
PublisherIEEE
ISSN (Print)2153-0866

Conference

Conference2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2016
CountryKorea, Republic of
CityDaejeon
Period9/10/1614/10/16

Bibliographical note

©2016 Crown

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  • Cite this

    Coppola, C., Faria, D. R., Nunes, U., & Bellotto, N. (2016). Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data. In IROS 2016 - 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5055-5061). (IEEE International Conference on Intelligent Robots and Systems. Proceedings). IEEE. https://doi.org/10.1109/IROS.2016.7759742