Automatic Detection of Human Interactions from RGB-D Data for Social Activity Classification

Claudio Coppola, Serhan Cosar, Diego R. Faria, Nicola Bellotto

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

Abstract

We present a system for temporal detection of social interactions. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications, it is important to be able to move to
more realistic data. For this reason, the proposed approach temporally detects intervals where individual or social activity is occurring. Recognition of human activities is a key feature for analysing the human behaviour. In particular, recognition of social activities is useful to trigger human-robot interactions
or to detect situations of potential danger. Based on that, this research has three goals: (1) define a new set of descriptors, which are able to characterise human interactions; (2) develop a computational model to segment temporal intervals with
social interaction or individual behaviour; (3) provide a public
dataset with RGB-D data with continuous stream of individual
activities and social interactions. Results show that the proposed
approach attained relevant performance with temporal
segmentation of social activities.
Original languageEnglish
Title of host publicationIEEE RO-MAN'17: IEEE International Symposium on Robot and Human Interactive Communication, Lisbon, Portugal
PublisherIEEE
ISBN (Electronic)978-1-5386-3518-6
ISBN (Print) 978-1-5386-3519-3
DOIs
Publication statusE-pub ahead of print - 14 Dec 2017

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    Coppola, C., Cosar, S., Faria, D. R., & Bellotto, N. (2017). Automatic Detection of Human Interactions from RGB-D Data for Social Activity Classification. In IEEE RO-MAN'17: IEEE International Symposium on Robot and Human Interactive Communication, Lisbon, Portugal IEEE. https://doi.org/10.1109/ROMAN.2017.8172405