Group Behavior Recognition for Gesture Analysis

Kosta, G.; Macq, Benoît

doi:10.1109/tcsvt.2007.913968

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The previous works mostly investigated the user behavior using the general digital game, and they are not explored user behavior in the environment of digital gesture based-game. Furthermore, this work has identified that user behavior can be detected by using human gesture such as a study in [5][6][7]. These studies demonstrated the way on how detecting user behavior by analyzing human expressions, gesture, and actions.…”

Section: Introductionmentioning

confidence: 90%

Using Gameplay Data for Identifying User’s Learning Experience in an Environment of Digital Gesture Based-game

Gani

Tomimatsu

2019

IJAE

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The digital game has become one of the most popular tools for teaching and learning. While digital game has accepted as a powerful tool for enhancing the process of teaching and learning, the nature of user learning experience is not well understood. In this regard, the goal of this study is to propose a method for identifying user learning experience using gameplay data. This study investigates possibility of identifying user learning experience namely, user learning behavior and user learning styles in environment of digital gesture based-game. This work presents computational theory of perception for identifying user learning behavior and user learning styles. The results showed that GLMP1 method is worthy to identify user learning behavior. Additionally, GLMP2 method offers 18% accuracy to identify user learning style. In conclusion, two proposed methods have the potential for identifying user learning experience in the environment of digital gesture based-game and another digital game.

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Section: Introductionmentioning

confidence: 90%

Using Gameplay Data for Identifying User’s Learning Experience in an Environment of Digital Gesture Based-game

Gani

Tomimatsu

2019

IJAE

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“…The recognition of human actions from videos has several interesting and important applications, like improving video content-based indexing and retrieval [1], helping in the identification of abnormal behavior in surveillance environments [2], enhancing human-computer interaction [3], remotely monitoring elderly people [4] or analyzing motion patterns of people with motor problems [5].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Frames in a Bag-of-Visual-Features Approach for Human Actions Recognition

Lopes

Oliveira

Almeida

et al. 2009

2009 XXII Brazilian Symposium on Computer Graphics and Image Processing

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Abstract-The recognition of human actions from videos has several interesting and important applications, and a vast amount of different approaches has been proposed for this task in different settings. Such approaches can be broadly categorized in model-based and model-free. Typically, model-based approaches work only in very constrained settings, and because of that, a number of model-free approaches appeared in the last years. Among them, those based in bag-of-visual-features (BoVF) have been proving to be the most consistently successful, being used by several independent authors.For videos to be represented by BoVFs, though, an important issue that arises is how to represent dynamic information. Most existing proposals consider the video as a spatio-temporal volume and then describe "volumetric patches" around 3D interest points. In this work, we propose to build a BoVF representation for videos by collecting 2D interest points directly. The basic idea is to gather such points not only from the traditional frames (xy planes), but also from those planes along the time axis, which we call the spatio-temporal frames. Our assumption is that such features are able to capture dynamic information from the videos, and are therefore well-suited to recognize human actions from them, without the need of 3D extensions for the descriptors. In our experiments, this approach achieved stateof-the-art recognition rates on a well-known human actions database, even when compared to more sophisticated schemes.

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Understanding the Data Needs for Developing a Computational Model of Team Dynamics

Fan,

Hui

2023

2023 IEEE Frontiers in Education Conference (FIE)

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Group Behavior Recognition for Gesture Analysis

Cited by 6 publications

References 15 publications

Using Gameplay Data for Identifying User’s Learning Experience in an Environment of Digital Gesture Based-game

Using Gameplay Data for Identifying User’s Learning Experience in an Environment of Digital Gesture Based-game

Spatio-Temporal Frames in a Bag-of-Visual-Features Approach for Human Actions Recognition

Understanding the Data Needs for Developing a Computational Model of Team Dynamics

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