• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.739-748
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• 2014

In this paper, we propose pyramid appearance and global structure action descriptors on both RGB and depth motion history images and a model-free method for human action recognition. In proposed algorithm, we firstly construct motion history image for both RGB and depth channels, at the same time, depth information is employed to filter RGB information, after that, different action descriptors are extracted from depth and RGB MHIs to represent these actions, and then multimodality information collaborative representation and recognition model, in which multi-modality information are put into object function naturally, and information fusion and action recognition also be done together, is proposed to classify human actions. To demonstrate the superiority of the proposed method, we evaluate it on MSR Action3D and DHA datasets, the well-known dataset for human action recognition. Large scale experiment shows our descriptors are robust, stable and efficient, when comparing with the-state-of-the-art algorithms, the performances of our descriptors are better than that of them, further, the performance of combined descriptors is much better than just using sole descriptor. What is more, our proposed model outperforms the state-of-the-art methods on both MSR Action3D and DHA datasets.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.9-16
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• 2010

A major purpose of management or occupational safety is a significant decrease in safety accidents. With this view, the establishment of occupational safety culture and the building of occupational communication network stand out as being more important than the past. This study has analysed the positive effects of occupational safety communication on safety consciousness and action of the employees in workplace. And it is confirmed that the occupational safety communication in workplace is the essential mechanism, through which the workers internalize safety consciousness and act safely. The safety consciousness and action of the employees are formed in safety culture, which is not only legal regulations, but a daily communication network in workplace. In these sense, the building of the occupational safety communication network is decisive for the establishment of safety culture. For these reasons, this study makes the proposition that a firm promotion of occupational communication network is necessary, which connects the safety culture and a effective safety management in workplace.

• Journal of the Korea Construction Safety Engineering Association
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• s.52
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• pp.48-57
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• 2011

A major purpose of management or occupational safety is a significant decrease in safety accidents. With this view, the establishment of occupational safety culture and the building of occupational communication network stand out as being more important than the past. This study has analysed the positive effects of occupational safety communication on safety consciousness and action of the employees in workplace. And it is confirmed that the occupational safety communication in workplace is the essential mechanism, through which the workers internalize safety consciousness and act safely. The safety consciousness and action of the employees are formed in safety culture, which is not only legal regulations, but a daily communication network in workplace. In these sense, the building of the occupational safety communication network is decisive for the establishment of safety culture. For these reasons, this study makes the proposition that a firm promotion of occupational communication network is necessary, which connects the safety culture and a effective safety management in workplace.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.29-38
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• 2015

Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.509-514
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• 2006

Human action recognition is an active research area in computer vision. In this paper, we present a robust method for human action recognition by using combined information of human body shape and motion information with multiple views image sequence. The principal component analysis is used to extract the shape feature of human body and multiple block motion of the human body is used to extract the motion features of human. This combined information with multiple view sequences enhances the recognition of human action. We represent each action using a set of hidden Markov model and we model each action by multiple views. This characterizes the human action recognition from arbitrary view information. Several daily actions of elderly persons are modeled and tested by using this approach and they are correctly classified, which indicate the robustness of our method.

• 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.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.423-430
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• 2021

Aiming Graph convolutional networks (GCNs) have achieved outstanding performances on skeleton-based action recognition. However, several problems remain in existing GCN-based methods, and the problem of low recognition rate caused by single input data information has not been effectively solved. In this article, we propose a Dual-stream fusion method that combines video data and skeleton data. The two networks respectively identify skeleton data and video data and fuse the probabilities of the two outputs to achieve the effect of information fusion. Experiments on two large dataset, Kinetics and NTU-RGBC+D Human Action Dataset, illustrate that our proposed method achieves state-of-the-art. Compared with the traditional method, the recognition accuracy is improved better.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.113-124
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• 2009

How can action learning program promote organizational learning performance and especially project based team performance in Web-based small and medium-sized enterprises (SMEs)? This article discusses the association between project based team in action learning program and the performance of Web-based SME to be learning organization. In the case of ALPACO, action learning program that promote employee communication behavior, knowledge sharing, and organizational learning are found to be positively associated with the project based team performance and organizational learning, The results indicate that action learning program in SMEs indeed associated with greater knowledge sharing, learning communication skills and changing organizational culture. Learning organization can be, in turn, positively developed by project based team through action learning program for creating competitive advantage, Also, this study offers further support for the practical perspective on learning organization performance. The evidence from this case study suggests that the project team in action learning program playa significant role in team performance and the development of learning organization of the firm. Therefore, in the future, Web-based SMEs should consider making investments in action learning program that encourage project team's effective management in decision making, knowledge sharing, and organizational learning.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.212-217
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• 2019

This study is a convergence study attempted to understand the learning effects of oral health education class applying action learning on the communication ability and problem-solving ability in dental hygiene students. The subjects of this study were 37 students in the third year of dental hygiene department. As a result, the learning effects of oral health education class applying action learning on the communication ability(p<0.001) and problem-solving ability(p<0.001) showed positive changes in the pre and post comparison. The changes in the scores of sub-dimensions of the communication ability and problem-solving ability were also significant in the pre and post comparison. As the class applying action learning is effective in improving the learner's communication ability and problem-solving ability, it should be utilized as the leaner participation-oriented teaching method for design and operation of dental hygiene education.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1851-1863
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• 2015

– In the paper, to handle the problem of traditional bag-of-features model ignoring the spatial relationship of local features in human action recognition, we proposed a Multiscale Spatial Position Coding under Locality Constraint method. Specifically, to describe this spatial relationship, we proposed a mixed feature combining motion feature and multi-spatial-scale configuration. To utilize temporal information between features, sub spatial-temporal-volumes are built. Next, the pooled features of sub-STVs are obtained via max-pooling method. In classification stage, the Locality-Constrained Group Sparse Representation is adopted to utilize the intrinsic group information of the sub-STV features. The experimental results on the KTH, Weizmann, and UCF sports datasets show that our action recognition system outperforms the classical local ST feature-based recognition systems published recently.