Decoding Children's Social Behavior

Rehg, James M.; Abowd, Gregory D.; Rozga, Agata; Romero, Mario; Clements, Mark A.; Sclaroff, Stan; Essa, Irfan; Ousley, Opal Y.; Li, Yin; Kim, Chanho; Rao, Hrishikesh; Kim, Jonathan C.; Presti, Liliana Lo; Zhang, Jianming; Lantsman, Denis; Bidwell, Jonathan; Ye, Zhefan

doi:10.1109/cvpr.2013.438

Cited by 142 publications

(90 citation statements)

References 21 publications

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“…Game settings can vary greatly, from professional sport scenarios, where automatic behavior analysis has been used to aid in understanding team strategies [8,26], to playground games such as tag, Marco Polo or hide-and-seek, where automatic recognition has been used to monitor children's social skills and diagnose social conditions such as autism [28,35,38]. At their most basic level, games require some level of physical exertion (e.g.…”

Section: Behavior Analysis In Gamesmentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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Tag is a popular children's playground game. It revolves around taggers that chase and then tag runners, upon which their roles switch. There are many variations of the game that aim to keep children engaged by presenting them with challenges and different types of gameplay. We argue that the introduction of sensing and floor projection technology in the playground can aid in providing both variation and challenge. To this end, we need to understand players' behavior in the playground and steer the interactions using projections accordingly. In this paper, we first analyze the behavior of taggers and runners in a traditional tag setting. We focus on behavioral cues that differ between the two roles. Based on these, we present a probabilistic role recognition model. We then move to an interactive setting and evaluate the model on tag sessions in an interactive tag playground. Our model achieves 77.96 % accuracy, which demonstrates the feasibility of our approach. We identify several avenues for improvement. Eventually, these should lead to a more thorough understanding of what happens in the playground, not only regarding player roles but also when the play breaks down, for example when players are bored or cheat.

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Section: Behavior Analysis In Gamesmentioning

confidence: 99%

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Moreno

Poppe

2016

J Multimodal User Interfaces

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“…Action recognition emerge as an important problem in a variety of computer vision applications ranging from visual surveillance [3], video understanding [4,5], learning social interactions [6], and diagnostics of mental disorders [7] to name a few. Over the past decade, the problem of action recognition in videos has been looked at from multiple perspectives, with a diverse set of solutions suggested.…”

Section: Related Workmentioning

confidence: 99%

Action recognition using global spatio-temporal features derived from sparse representations

Somasundaram

Cherian

Morellas

et al. 2014

Computer Vision and Image Understanding

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Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to alleviate this difficulty is to focus the computations to informative (salient) regions of the video. In this paper, we propose a novel global spatio-temporal selfsimilarity measure to score saliency using the ideas of dictionary learning and sparse coding. In contrast to existing methods that use local spatio-temporal feature detectors along with descriptors (such as HOG, HOG3D, HOF, etc.), dictionary learning helps consider the saliency in a global setting (on the entire video) in a computationally efficient way. We consider only a small percentage of the most salient (least self-similar) regions found using our algorithm, over which spatio-temporal descriptors such as HOG and region covariance descriptors are computed. The ensemble of such block descriptors in a bag-of-features framework provides a holistic description of the motion sequence which can be used in a classification setting. Experiments on several benchmark datasets in video based action classification demonstrate that our approach performs competitively to the state of the art.

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“…The adult engages the child in various activities, following the same protocol as our earlier MMDB work 7 . See [31] for a detailed description of the protocol. All child subjects participated with consent from their parents.…”

Section: A Data Collection and Annotationmentioning

confidence: 99%

Detecting bids for eye contact using a wearable camera

Liu

et al. 2015

2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)

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Abstract-We propose a system for detecting bids for eye contact directed from a child to an adult who is wearing a point-of-view camera. The camera captures an egocentric view of the child-adult interaction from the adult's perspective. We detect and analyze the child's face in the egocentric video in order to automatically identify moments in which the child is trying to make eye contact with the adult. We present a learning-based method that couples a pose-dependent appearance model with a temporal Conditional Random Field (CRF). We present encouraging findings from an experimental evaluation using a newly collected dataset of 12 children. Our method outperforms state-of-the-art approaches and enables measuring gaze behavior in naturalistic social interactions.

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Decoding Children's Social Behavior

Abstract: We introduce a new problem domain for activity recognition

Cited by 142 publications

References 21 publications

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Automatic behavior analysis in tag games: from traditional spaces to interactive playgrounds

Action recognition using global spatio-temporal features derived from sparse representations

Detecting bids for eye contact using a wearable camera

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