IEICE TRANSACTIONS on Information
An Adaptive Visual Attentive Tracker with HMM-Based TD Learning Capability for Human Intended Behavior
Summary :
In the study, we build a system called Adaptive Visual Attentive Tracker (AVAT) for the purpose of developing a non-verbal communication channel between the system and an operator who presents intended movements. In the system, we constructed an HMM (Hidden Markov Models)-based TD (Temporal Difference) learning algorithm to track and zoom in on an operator's behavioral sequence which represents his/her intention. AVAT extracts human intended movements from ordinary walking behavior based on the following two algorithms: the first is to model the movements of human body parts using HMMs algorithm, and the second is to learn the model of the tracker's action using a model-based TD learning algorithm. In the paper, we describe the integrated algorithm of the above two methods: whose linkage is established by assigning the state transition probability in HMM as a reward in TD learning. Experimental results of extracting an operator's hand sign action sequence during her natural walking motion are shown which demonstrates the function of AVAT as it is developed within the framework of perceptual organization. Identification of the sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.6 pp.1051-1058
- Publication Date
- 2003/06/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Cognitive Science
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Minh Anh Thi HO, Yoji YAMADA, Yoji UMETANI, "An Adaptive Visual Attentive Tracker with HMM-Based TD Learning Capability for Human Intended Behavior" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 6, pp. 1051-1058, June 2003, doi: .
Abstract: In the study, we build a system called Adaptive Visual Attentive Tracker (AVAT) for the purpose of developing a non-verbal communication channel between the system and an operator who presents intended movements. In the system, we constructed an HMM (Hidden Markov Models)-based TD (Temporal Difference) learning algorithm to track and zoom in on an operator's behavioral sequence which represents his/her intention. AVAT extracts human intended movements from ordinary walking behavior based on the following two algorithms: the first is to model the movements of human body parts using HMMs algorithm, and the second is to learn the model of the tracker's action using a model-based TD learning algorithm. In the paper, we describe the integrated algorithm of the above two methods: whose linkage is established by assigning the state transition probability in HMM as a reward in TD learning. Experimental results of extracting an operator's hand sign action sequence during her natural walking motion are shown which demonstrates the function of AVAT as it is developed within the framework of perceptual organization. Identification of the sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_6_1051/_p
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@ARTICLE{e86-d_6_1051,
author={Minh Anh Thi HO, Yoji YAMADA, Yoji UMETANI, },
journal={IEICE TRANSACTIONS on Information},
title={An Adaptive Visual Attentive Tracker with HMM-Based TD Learning Capability for Human Intended Behavior},
year={2003},
volume={E86-D},
number={6},
pages={1051-1058},
abstract={In the study, we build a system called Adaptive Visual Attentive Tracker (AVAT) for the purpose of developing a non-verbal communication channel between the system and an operator who presents intended movements. In the system, we constructed an HMM (Hidden Markov Models)-based TD (Temporal Difference) learning algorithm to track and zoom in on an operator's behavioral sequence which represents his/her intention. AVAT extracts human intended movements from ordinary walking behavior based on the following two algorithms: the first is to model the movements of human body parts using HMMs algorithm, and the second is to learn the model of the tracker's action using a model-based TD learning algorithm. In the paper, we describe the integrated algorithm of the above two methods: whose linkage is established by assigning the state transition probability in HMM as a reward in TD learning. Experimental results of extracting an operator's hand sign action sequence during her natural walking motion are shown which demonstrates the function of AVAT as it is developed within the framework of perceptual organization. Identification of the sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns.},
keywords={},
doi={},
ISSN={},
month={June},}
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TY - JOUR
TI - An Adaptive Visual Attentive Tracker with HMM-Based TD Learning Capability for Human Intended Behavior
T2 - IEICE TRANSACTIONS on Information
SP - 1051
EP - 1058
AU - Minh Anh Thi HO
AU - Yoji YAMADA
AU - Yoji UMETANI
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2003
AB - In the study, we build a system called Adaptive Visual Attentive Tracker (AVAT) for the purpose of developing a non-verbal communication channel between the system and an operator who presents intended movements. In the system, we constructed an HMM (Hidden Markov Models)-based TD (Temporal Difference) learning algorithm to track and zoom in on an operator's behavioral sequence which represents his/her intention. AVAT extracts human intended movements from ordinary walking behavior based on the following two algorithms: the first is to model the movements of human body parts using HMMs algorithm, and the second is to learn the model of the tracker's action using a model-based TD learning algorithm. In the paper, we describe the integrated algorithm of the above two methods: whose linkage is established by assigning the state transition probability in HMM as a reward in TD learning. Experimental results of extracting an operator's hand sign action sequence during her natural walking motion are shown which demonstrates the function of AVAT as it is developed within the framework of perceptual organization. Identification of the sign gesture context through wavelet analysis autonomously provides a reward value for optimizing AVAT's action patterns.
ER -
English
