Hidden Markov model based continuous online gesture recognition

Eickeler, Stefan; Kosmala, A.; Rigoll, Gerhard

doi:10.1109/icpr.1998.711914

Cited by 78 publications

(44 citation statements)

References 6 publications

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“…HMMs have been widely and successfully applied to a large number of problems, such as speech recognition [4], DNA and protein modeling [5], and gesture recognition [6]. We show in this paper that HMMs are also effective in classification of MMOG players, and, in particular, have higher recognition performance than AMBR based on action frequencies.…”

Section: Introductionmentioning

confidence: 71%

MMOG Player Classification Using Hidden Markov Models

Matsumoto

Thawonmas

2004

Entertainment Computing – ICEC 2004

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Abstract. In this paper, we describe our work on classification of players in Massively Multiplayer Online Games using Hidden Markov Models based on player action sequences. In our previous work, we have discussed a classification approach using a variant of Memory Based Reasoning based on player action frequencies. That approach, however, does not exploit time structures hidden in action sequences of the players. The experimental results given in this paper show that Hidden Markov Models have higher recognition performance than our previous approach, especially for classification of players of different types but having similar action frequencies.

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Section: Introductionmentioning

confidence: 71%

MMOG Player Classification Using Hidden Markov Models

Matsumoto

Thawonmas

2004

Entertainment Computing – ICEC 2004

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“…A user interface based on gestural input from human has been researched in [4]- [6]. By using hand gesture such as sign language, human or user can convey information naturally to the system.…”

Section: A Related Workmentioning

confidence: 99%

Pointing Angle Estimation for Human-Robot Interface

Nor¹,

Maeda²,

Mizukawa³

2013

JACN

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Abstract-In this paper, we propose an approach to track and estimate user pointing direction in 3D Space. In the area of human-robot interaction, user communicates with service robot in their daily life activities to give commands and execute the given task accordingly. Therefore, the ability of user to gives command to service robot naturally can provide an interactive user interface system for real 3D space environment. For this purpose, we aim to perform pointing gesture tracking and after that estimate the user's pointing direction. Our method of pointing direction estimation is based on 3D orientation of hand and shoulder center of user. We make comparison with our previous method to find the best hypothesis. Experimental results show the angular error for the estimation of pointing direction is successfully improved from our previous method. As a result, our natural user interface system can manipulate 3D objects in living room environment thus providing intuitive robotic service for human robot interaction.Index Terms-Human-robot interaction, pointing gesture, user tracking.

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“…Temporal templates have been proposed and used to categorize actions [5]. Methods that explicitly model relative changes in spatial descriptors over time [1], or estimates of global and local motion [10,14] have also been used. Methods that use the silhouette of the body to construct more sophisticated representation for human action have been proposed [36].…”

Section: Introductionmentioning

confidence: 99%

Modeling Motion of Body Parts for Action Recognition

Tran

Kakadiaris

Shah

2011

Procedings of the British Machine Vision Conference 2011

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This paper presents a simple and computationally efficient framework for human action recognition based on modeling the motion of human body parts. Intuitively, a collective understanding of human part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of the human body parts to learn and classify human actions. The proposed representation combines the advantages of both local and global representations, encoding the relevant motion information as well as being robust to local appearance changes. Our work is motivated by the pictorial structures model and the framework of sparse representations for recognition. Human part movements are represented efficiently through quantization in the polar space. The key discrimination within each action is efficiently encoded by sparse representation to perform classification. The proposed method is evaluated on both the KTH and the UCF action datasets and the results are compared against other state-of-the-art methods.

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Hidden Markov model based continuous online gesture recognition

Cited by 78 publications

References 6 publications

MMOG Player Classification Using Hidden Markov Models

MMOG Player Classification Using Hidden Markov Models

Pointing Angle Estimation for Human-Robot Interface

Modeling Motion of Body Parts for Action Recognition

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