Wizard of Wii

Cheema, Salman; LaViola, Joseph J.

doi:10.1145/2159365.2159407

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…The linear classifier of Rubine [1991]] has been utilized in both 2D [Mertz and Lecoanet 1997;Pereira et al 2004] and 3D [Cheema and LaViola 2011;Hoffman et al 2010;Sreedharan et al 2007] gesture recognition applications. In addition, DTW has been used in several different applications, including those in data mining, manufacturing, robotics, medicine, and gesture recognition [Keogh and Pazzani 2001;Sakoe and Chiba 1978].…”

Section: Linear Svm and Dtw Classifiersmentioning

confidence: 99%

“…Sart et al [2010] first investigated GPU-and FPGA-based DTW approaches in data mining. SVM has also been used in 3DUI, for example by Dardas and Georganas [2011], to detect 10 hand gestures with a multiclass SVM utilizing bag-of-features. Huang et al [2009] use Gabor filters with PCA and a 1-vs.-1 multiclass SVM to recognize 11 gestures.…”

Section: Linear Svm and Dtw Classifiersmentioning

confidence: 99%

“…Although there has been a significant amount of work in the area of 3D gesture recognition over the years [Bigdelou et al 2012b;Cheema and LaViola 2011;Hoffman et al 2010;Kwon 2008], the ability to accurately recognize a large set of 3D gestures (e.g., more than 30) is still an open research problem. As the gesture set size increases, the recognition accuracy will tend to decease rapidly.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Benefits of Context in 3D Gesture Recognition for Game-Based Virtual Environments

Taranta

Simons

Sukthankar

et al. 2015

ACM Trans. Interact. Intell. Syst.

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We present a systematic exploration of how to utilize video game context (e.g., player and environmental state) to modify and augment existing 3D gesture recognizers to improve accuracy for large gesture sets. Specifically, our work develops and evaluates three strategies for incorporating context into 3D gesture recognizers. These strategies include modifying the well-known Rubine linear classifier to handle unsegmented input streams and per-frame retraining using contextual information (CA-Linear); a GPU implementation of dynamic time warping (DTW) that reduces the overhead of traditional DTW by utilizing context to evaluate only relevant time sequences inside of a multithreaded kernel (CA-DTW); and a multiclass SVM with per-class probability estimation that is combined with a contextually based prior probability distribution (CA-SVM).We evaluate each strategy using a Kinect-based third-person perspective VE game prototype that combines parkour-style navigation with hand-to-hand combat. Using a simple gesture collection application to collect a set of 57 gestures and the game prototype that implements 37 of these gestures, we conduct three experiments. In the first experiment, we evaluate the effectiveness of several established classifiers on our gesture set and demonstrate state-of-the-art results using our proposed method. In our second experiment, we generate 500 random scenarios having between 5 and 19 of the 57 gestures in context. We show that the contextually aware classifiers CA-Linear, CA-DTW, and CA-SVM significantly outperform their noncontextually aware counterparts by 37.74%, 36.04%, and 20.81%, respectively. On the basis of the results of the second experiment, we derive upper-bound expectations for in-game performance for the three CA classifiers: 96.61%, 86.79%, and 96.86%, respectively. Finally, our third experiment is an in-game evaluation of the three CA classifiers with and without context. Our results show that through the use of context, we are able to achieve an average in-game recognition accuracy of 89.67% with CA-Linear compared to 65.10% without context, 79.04% for CA-DTW compared to 58.1% without context, and 90.85% with CA-SVM compared to 75.2% without context.

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Section: Linear Svm and Dtw Classifiersmentioning

confidence: 99%

Section: Linear Svm and Dtw Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Benefits of Context in 3D Gesture Recognition for Game-Based Virtual Environments

Taranta

Simons

Sukthankar

et al. 2015

ACM Trans. Interact. Intell. Syst.

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show abstract

“…The features need to be extended to capture 3D information with the main classifier and training algorithm remaining the same. This approach has been used successfully in developing simple, yet effective 3D gesture recognizers [112,125,126].…”

Section: Other Learning-based Techniquesmentioning

confidence: 99%

“…As seen in Section 4, researchers have been able to get very high accuracy rates. However, we have also seen from Cheema et al [125,126] The open research problem here is how to design an ecologically valid experiment to test a 3D gestural interface. To illustrate the challenge, consider a 3D gestural interface for a video game.…”

Section: Ecologicalmentioning

confidence: 99%

3D Gestural Interaction: The State of the Field

LaViola

2013

ISRN Artificial Intelligence

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3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures so that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state of the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and how to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area.

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