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

Coppola, Claudio and Faria, Diego and Nunes, Urbano and Bellotto, Nicola (2016) Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9-14 October 2016, Daejeon, Korea.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive

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 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 a modified dynamic Bayesian mixture model designed to merge features with different semantics and also with proximity priors, the proposed framework can correctly recognize social activities in different situations, e.g. using data from one or two individuals.

Keywords:Gesture, Posture, Social Spaces, Human-Robot Interaction, Healthcare, Assisted Living
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:23425
Deposited On:06 Jul 2016 18:59

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