• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.281-290
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• 2015

Human action recognition from a video scene has remained a challenging problem in the area of computer vision and pattern recognition. The development of the low-cost RGB depth camera (RGB-D) allows new opportunities to solve the problem of human action recognition. In this paper, we present a comprehensive review of recent approaches to human action recognition based on depth maps, skeleton joints, and other hybrid approaches. In particular, we focus on the advantages and limitations of the existing approaches and on future directions.

• Journal of Korea Game Society
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• v.8 no.2
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• pp.47-55
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• 2008

This paper introduces that SSFE(Skeleton System for Facial Expression) to deform facial expressions by rigging of skeletons does same functions with 14 facial muscles based on anatomy. A three dimensional animation tool (MAYA 8.5) is utilized for making the SSFE that presents deformation of mesh models implementing facial expressions around eyes, nose and mouse. The SSFE has a good reusability within diverse human mesh models. The reusability of SSFE can be understood as OSMU(One Source Multi Use) of three dimensional animation production method. It can be a good alternative technique for reducing production budget of animations. It can also be used for three dimensional animation industries such as virtual reality and game.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.257-260
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• 2005

This paper discusses a human body extraction method for mobile robot system. The skeleton features are used to analyze human motion and pose estimation. The intelligent robot system requires more robust silhouette extraction method because it has internal vibration and low resolution. The new hybrid silhouette extraction method is proposed to overcome this constrained environment. Finally, the experimental results show the superiority of the Proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.152-158
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• 2009

In this paper, the right arm was modelled by muscle-skeleton elements to obtain the behavior of right arm of human under impact condition, where physical and geometrical properties of human body such as Young's modulus, shear modulus, cross sectional area, length, density, moment of inertia and position were defined. Based on the numerical model of the right arm, the impact response of the right arm was obtained. By the comparison with the experimental results, the model of the right arm was verified.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3924-3940
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• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1257-1263
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• 2012

An implementation of animation module using the 3D body data scanned by laser scanner is reported in this paper. Characteristic points of the skeleton in human body were picked up as pivot point for 3D rotation. The body data set wes reconstructed as objects built in hierarchical tree structure, which is based on skeleton model. In order to implement the 3D animation of the laser scanned body data, the vertexes of the objects were connected as skeleton structure and animated to follow dynamic patterns inputted by user.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.551-560
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• 2023

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.