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Decoding Children's Social Behavior

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jun 2013 Sweden, Italy Publisher:IEEEJournal:2013 IEEE Conference on Computer Vision and Pattern RecognitionFunded by:NSF | Collaborative Research: C...

Authors: Rehg, J.; Abowd, G.; Rozga, A.; Romero, M.; Clements, M; Sclaroff, S.; Essa, I; +10 Authors

doi: 10.1109/cvpr.2013.438

handle: 10447/97700

Decoding Children's Social Behavior

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Abstract

We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.

Countries

Sweden, Italy

Related Organizations

Boston University
United States
Georgia Institute of Technology
United States
Emory University
United States
Boston College
United States
University of Palermo
Italy

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Keywords

Behavior, Psychology, Dataset, Video analysis, Speech Analysis, Autism, Behavior, Inter-action protocols, Social and communicative behavior, Autism, Developmental disorders, Video analysis, Annan data- och informationsvetenskap, Semi-structured, Speech Analysis, Research questions, Technical challenges, Activity recognition, Psychology, Social behavior, Other Computer and Information Science, Dataset

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	132
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%