Automatic detection of human interactions from RGB-D data for social activity classification

Coppola, Claudio, Cosar, Serhan, Faria, Diego and Bellotto, Nicola (2017) Automatic detection of human interactions from RGB-D data for social activity classification. In: IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 28 Aug - 1 Sep 2017, Lisbon.

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Item Type:Conference or Workshop contribution (Paper)
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We present a system for the 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, it is important to be able to detect temporally the intervals of time in which humans are performing an individual activity or a social one. Recognition of the human activities is a key feature for analysing the human behaviour. In particular, recognition of social activities could be 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 able to represent the phenomena; (2) develop a computational model able to discern the intervals in which a pair of people are interacting or performing individual activities; (3) provide a public dataset with RGB-D videos where social interactions and individual activities happen in a continuous stream. Results show that using the proposed approach allows to reach a good performance in the temporal segmentation of social activities.

Keywords:Social Activity Classification, RGB-D sensing, Human Activity Recognition
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:27647
Deposited On:07 Jun 2017 15:02

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