Double articulation analyzer for unsegmented human motion using Pitman-Yor language model and infinite hidden Markov model

Taniguchi, Tadahiro; Nagasaka, Shogo

doi:10.1109/sii.2011.6147455

Cited by 33 publications

(29 citation statements)

References 13 publications

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“…Takano et al [1] used HMM for modeling driving behavior and reported that it is difficult to use their prediction method with the degree of accuracy for automated driving system. HDS including Piece Wise Auto Regressive eXogenous model (PWARX) [2], Stochastic Switched Auto Regressive eXogenous [3] and Auto Regressive Hidden Markov Model [4] This research was partially supported by a Grant-in-Aid Creative Scientific Research 2007-2011 funded by the Ministry of Education, Culture, Sports, Science and Technology, Japan.…”

Section: A Prediction Of Driving Datamentioning

confidence: 99%

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Semiotic prediction of driving behavior using unsupervised double articulation analyzer

Taniguchi

Nagasaka

Hitomi

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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In this paper, we propose a novel semiotic prediction method for driving behavior based on double articulation structure. It has been reported that predicting driving behavior from its multivariate time series behavior data by using machine learning methods, e.g., hybrid dynamical system, hidden Markov model and Gaussian mixture model, is difficult because a driver's behavior is affected by various contextual information. To overcome this problem, we assume that contextual information has a double articulation structure and develop a novel semiotic prediction method by extending nonparametric Bayesian unsupervised morphological analyzer. Effectiveness of our prediction method was evaluated using synthetic data and real driving data. In these experiments, the proposed method achieved long-term prediction 2-6 times longer than some conventional methods.

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Section: A Prediction Of Driving Datamentioning

confidence: 99%

“…Taniguchi and Nagasaka [7] proposed a double articulation analyzer for extracting long-term human motion chunks by connecting several short-term natural segments of human motion. The method is based on analyzing hidden double articulation structure, which is well known in Semiotics.…”

Section: B Prediction Based On Double Articulationmentioning

confidence: 99%

Semiotic prediction of driving behavior using unsupervised double articulation analyzer

Taniguchi

Nagasaka

Hitomi

et al. 2012

2012 IEEE Intelligent Vehicles Symposium

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“…The authors have proposed to predict a human motion in the future using the motion symbols. In [9], the double articulation analyzer of human motions has been proposed to do segmentation and categorization of human motions. These works are significant enough to pursue since they provide a key to connect human motions and symbols (language).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, considerable research on the modeling of human motions has also been conducted [7]- [9]. Although this paper focuses on the categorization of perceptual information, the difference lies in the fact that the proposed model aims at simultaneously categorizing different kinds of concepts and learning their relationships.…”

Section: Introductionmentioning

confidence: 99%

Integrated concept of objects and human motions based on multi-layered multimodal LDA

Fadlil

Ikeda

Abe

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The human understanding of things is based on prediction which is made through concepts formed by the categorization of experience. To mimic this mechanism in robots, multimodal categorization, which enables the robot to form concepts, has been studied. On the other hand, segmentation and categorization of human motions have also been studied to recognize and predict future motions. This paper addresses the issue on how these different kinds of concepts are integrated to generate higher level concepts and, more importantly, on how the higher level concepts affect the formation of each lower level concept. To this end, we propose the multi-layered multimodal latent Dirichlet allocation (mMLDA), which is an expansion of the MLDA to learn and represent the hierarchical structure of concepts. We also examine a simple integration model and compare it with the mMLDA. The experimental results reveal that the mMLDA leads to a better inference performance and, indeed, forms higher level concepts which integrate motions and objects that are necessary for real-world understanding.

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“…[7] [8] [9] [10] [12] Parametric Bias Recurrent Neural Network RNNPB [13] RNNPB [13] RNNPB [13] [14] [15] human-object interaction HOI [16] [17] HOI z C ∼ Mult(θ …”

mentioning

confidence: 99%

Integrated Concept of Human Motions and Objects based on Multi-layered Multimodal LDA

Attamimi¹,

Fadlil²,

Abe³

et al. 2014

JRSJ

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The human understanding of things is based on prediction which is made through concepts formed by categorization of their experience. To mimic this mechanism in robots, multimodal categorization, which enables the robot to form concepts, has been studied. On the other hand, segmentation and categorization of human motions have also been studied to recognize and predict future motions. This paper addresses the issue of how these concepts are integrated to generate higher level concepts and, more importantly, how the higher level concepts affect each lower level concept formation. To this end, we propose multi-layered multimodal latent Dirichlet allocation (mMLDA) to learn and represent the hierarchical structure of concepts. We also examine a simple integration model and compare with the mMLDA. The experimental results reveal that the mMLDA leads to better inference performance and, indeed, forms higher level concepts integrating motions and objects that are necessary for real-world understanding.

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Double articulation analyzer for unsegmented human motion using Pitman-Yor language model and infinite hidden Markov model

Cited by 33 publications

References 13 publications

Semiotic prediction of driving behavior using unsupervised double articulation analyzer

Semiotic prediction of driving behavior using unsupervised double articulation analyzer

Integrated concept of objects and human motions based on multi-layered multimodal LDA

Integrated Concept of Human Motions and Objects based on Multi-layered Multimodal LDA

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