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Opal Y. Ousley

dblp:135/4874 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Video understanding and tracking · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Video understanding and tracking › activity recognition
multimodal activity recognition
0.212013
Decoding Children's Social Behavior · CVPR 2013
Computer vision › Video understanding and tracking › video analytics › behavior analysis › human behavior analysis
social behavior recognition
0.212013
Decoding Children's Social Behavior · CVPR 2013
Medical and health informatics › clinical diagnosis
autism spectrum disorder diagnosis
0.012013
Decoding Children's Social Behavior · CVPR 2013

Methods — techniques the papers use, named apart from their topics

multimodal fusion · 0.3
YearPublicationVenuePosition
2013 Decoding Children's Social Behavior
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.
James M. Rehg, Gregory D. Abowd, Agata Rozga, Mario Romero, Mark A. Clements, Stan Sclaroff, Irfan A. Essa, Opal Y. Ousley, Yin Li 0003, Chanho Kim, Hrishikesh Rao 0001, Jonathan C. Kim, Liliana Lo Presti, Jianming Zhang 0001, Denis Lantsman, Jonathan Bidwell, Zhefan Ye
CVPR8