• 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.

• Journal of the Korean Society of Safety
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• v.13 no.1
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• pp.112-118
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• 1998

Human errors performed during the operations have a dominant portion of the accidents. But the systematic human error evaluation methodology universally accepted is not developed yet. One of the difficulties in performing human reliability analysis is to evaluate the performance shaping factors which represent the characteristics and the circumstances in the discriminate manner. For assessing a specific human action more exactly, it is necessary to consider all of the PSFs at the same time which make an effect on the human action. In this paper, dynamic influence diagrams are introduced to model simultaneously their effects on the specific human action. And the human actions and their subsequent PSFs are categorized and classified as the complementary works. A new human error evaluation methodology using influence diagrams is developed. This methodology involves the categorization of PSFs and the PSFs quantification. The applied analysis results for the example task are shown for representative purposes. It is shown that this approach is very flexible in that it can be applied to any kind of actions.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1728-1735
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• 2009

Human action tracking plays an important place in human-computer-interaction, human action tracking is a challenging task because of the exponentially increased computational complexity in terms of the degrees of freedom of the object and the severe image ambiguities incurred by frequent self-occlusions. In this paper, we will propose a novel method to track human action, in our technique, a dynamic background estimation algorithm will be applied firstly. Based on the estimated background, we then extract the human object from the video sequence, and the skeletonization method and Hough transform method will be used to detect the main structure of human body and each part rotation angle. The calculated rotation angles will be used to control a crane in the port, thus we can just control the container crane by using signalman body. And the experimental results can show that our proposed method can get a preferable result than the conventional methods such as: MIT, JPF or MFMC.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.337-345
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• 2023

In this study, we formulated a method that evaluates Taekwondo Poomsae performance using a series of choreographed training movements. Despite recent achievements in 3D human pose estimation (HPE) performance, the analysis of human actions remains challenging. In particular, Taekwondo Poomsae action analysis is challenging owing to the absence of time synchronization data and necessity to compare postures, rather than directly relying on joint locations owing to differences in human shapes. To address these challenges, we first decomposed human joint representation into joint rotation (posture) and limb length (body shape), then synchronized a comparison between test and reference pose sequences using DTW (dynamic time warping), and finally compared pose angles for each joint. Experimental results demonstrate that our method successfully synchronizes test action sequences with the reference sequence and reflects a considerable gap in performance between practitioners and professionals. Thus, our method can detect incorrect poses and help practitioners improve accuracy, balance, and speed of movement.

• 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.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.229-234
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• 2012

Operator's action time is evaluated from MAAP4 analysis used in conventional probabilistic safety assessment(PSA) of a nuclear power plant. MAAP4 code which was developed for severe accident analysis is too conservative to perform a realistic PSA. A best-estimate code such as RELAP5/MOD3, MARS has been used to reduce the conservatism of thermal hydraulic analysis. In this study, operator's action time of core cooling recovery operation is evaluated by using the MARS code, which its Fussell-Vessely(F-V) value was evaluated as highly important in a small break loss of coolant(SBLOCA) event and loss of component cooling water(LOCCW) event in previous PSA. The main conclusions were elicited : (1) MARS analysis provides larger time window for operator's action time than MAAP4 analysis and gives the more realistic time window in PSA (2) Sufficient operator's action time can reduce human error probability and core damage frequency in PSA.

• Korean Journal of Human Ecology
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• v.22 no.2
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• pp.267-282
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• 2013

This study analyzed the effect of consumer value and unethicality on the type of consumer complaint behaviors. Despite the obvious importance of the research on consumer complaint behaviors focused on consumer's inherent personality, there is relatively little work done. The purpose of this study is to analyze the determinants of consumer complaint behaviors in order to improve consumers' well-being and develop the market condition. The 1,050 respondents are finally analyzed using the descriptive statistics, factor analysis, and multinominal logit model. Consumer value and unethicality are significant effect on the type of consumer complaint behaviors such as no action, private action only, public action only, and both private and action. The orientation of achievement and pleasure among consumers' value is associated with the higher level of complaint behaviors compared with no action. In terms of consumers' unethicality, no harm unethicality is associated with the types of each consumer complaint behavior except no action. On the other hand, both proactive and passive unethicality increase the possibility of no action. The policy implications of the consumer education are suggested as well as the directions of customer management strategies in the business sector.

• Science of Emotion and Sensibility
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• v.6 no.1
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• pp.55-62
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• 2003

This study was an exploratory approach to the cognitive model of textile designers on the stage of motif development in textile design process. Prior to the main research, several previous studies adopting methods of video/audio protocol analysis were reviewed. On the basis of the review, the categories of design action were derived as an analysis frame by application of top-down access method, meanwhile the sub-groups of each category of design action were identified through a bottom-up access method. To summarize the research result, total three categories of textile design action appeared based on the theory of ‘Human processor’ model : ‘motor action’, ‘perceptual action’ and 'cognitive action'. In next, a new coding scheme suitably explaining these three categories of fertile design action was developed. Finally, a cognitive model of textile designer on the stage of motif development, employing the new coding scheme, was suggested in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.