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


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.

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Divisions: College of Engineering & Physical Sciences
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ISBN: 978-1-5386-3519-3, 978-1-5386-3518-6
Last Modified: 17 Jun 2024 08:31
Date Deposited: 26 Oct 2017 09:50
PURE Output Type: Conference contribution
Published Date: 2017-12-14
Published Online Date: 2017-12-14
Accepted Date: 2017-09-01
Authors: Coppola, Claudio
Cosar, Serhan
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)
Bellotto, Nicola



Version: Accepted Version

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