• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.186-192
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• 2009

In this paper, we represent the recognition method for human frontal upper body pose. In HCI(Human Computer Interaction) and HRI(Human Robot Interaction) when a interaction is established the human has usually frontal direction to the robot or computer and use hand gestures then we decide to focus on human frontal upper-body pose, The two main difficulties are firstly human pose is consist of many parts which cause high DOF(Degree Of Freedom) then the modeling of human pose is difficult. Secondly the matching between image features and modeling information is difficult. Then using Pictorial Model we model the human main poses which are mainly took the space of frontal upper-body poses and we recognize the main poses by making main pose database. using determined main pose we used the model parameters for particle filter which predicts the posterior distribution for pose parameters and can determine more specific pose by updating model parameters from the particle having the maximum likelihood. Therefore based on recognizing main poses and tracking the specific pose we recognize the human frontal upper body poses.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.11-20
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• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.619-628
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• 2011

Normally vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment. On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

• Korean Journal of Human Ecology
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• v.7 no.3
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• pp.15-33
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• 2004

A precise understanding of the human form in static pose serves as the basis of designing clothing. When the human body is in motion, however, even an article of clothing designed to fit the human form in static pose can pull and change, thus restricting the body. In order to increase the fit of the clothing, which may be termed the second skin, its form and measurements therefore must be determined in correlation not only with the formal characteristics of the human body, in static pose but also with its functional characteristics in motion, as caused by the movements of the human body. In this study, the motion factor was selected as the primary basis for designing slacks with good fit in both static and moving states. By indentifying the areas in which lower limb movement cause significant changes in body surface lines, we suggest several application methods for designing slacks. Using unmarried female university students aged 18 - 24 as subjects, a total of 32 body surface categories (15 body surface lines and 17 body surface segment lines) were measured in one static and 9 movement poses. In particular, expansion and contraction levels and rates were measured and used in the analysis. The analysis first involved the calculation of the average measurement per body part in body surface line in static pose as well as of the average expansion and contraction levels and rates in 9 lower limb movements. Two-way MANOVA and multiple comparison analysis (Tukey) were conducted on movements and individual somatotypes regarding measurement per body part and expansion and contraction rates. Body parts whose measurements of body surface lines differed significantly in body surface line in static pose versus in movement were then identified. The results of this study are as follows. First, changes in body surface lines caused by lower limb movements were significant in all body surface lines of the lower trunk, both horizontal and vertical, with the exception of abdomen girth, midway thigh girth, ankle girth, hip length, and posterior knee girth. Second, significantly expanded 10 body surface lines in moving pose were detected and illustrated in table 4. These body parts should be studied in designing or pattern designing, especially for close-fitting pants, in using stretch fabric, and in sensory evaluation of good fit during movement.

• Fashion & Textile Research Journal
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• v.4 no.6
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• pp.544-549
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• 2002

By the movable thermal manikin developed by China Dong Hua university, the laws of clothing thermal insulation influenced by dressing poses are studied. It is found that $I_a$ on nude thermal manikin has no relation to testing pose as a whole (notable level is 5%), while the change of testing pose influences $I_a$ value on parts of body obviously. The testing result $I_{cle}$ on clothed thermal manikin has relation to testing pose. The $I_{cle}$ value of the whole body in seated pose decreases 20 percent compared with that in standing pose (notable level is 1%). In view of heat transmission theory, the reasons are pointed out based on the knowledge of heat transmission.

• Journal of the Korean Society of Clothing and Textiles
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• v.38 no.5
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• pp.769-782
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• 2014

This study considers the correlation between model pose and clothes in Patrick Demarchelier's fashion photos as well as expression characteristics. The conclusions of the study are as follows. The type of model pose in Patrick Demarchelier's fashion photos can be categorized into five types: maximized type of upper and lower body part, minimized type of upper and lower body part, maximized type of upper body and minimized type of lower body part, minimized type of upper body and maximized type of lower body part, basic type of upper and lower body part. In case of having examined the correlation between body movement and costume, the clothes in the model pose included in the maximization of the body were formed mainstream by silhouette, which was formed by decorative elements or full drapery. In the model poses included in the reduction of the body, the costume tended to expose many parts of the body to provide a simple or structural silhouette form. The costume was expressed in colorful form and poses assumed without body movements. The expression characteristics of the model poses in Patrick Demarchelier's fashion photos were sensuality, dynamicity, and simplicity. First, sensuality was expressed as feminine sensuality accompanying an erotic mood by naturally emphasizing a woman's breast or leg by reducing the body. Second, dynamicity provided a vividness to the image as if directly living and moving by highlighting the rhythmic aspect of the body. Simplicity aroused the effect of paying attention to clothes or other incidental elements rather than the image expressed by the body of a model by excluding body movement.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.95-100
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• 2018

In this paper, we propose a binarized multi-scale module to accelerate the speed of the pose estimating deep neural network. Recently, deep learning is also used for fine-tuned tasks such as pose estimation. One of the best performing pose estimation methods is based on the usage of two neural networks where one computes the heat maps of the body parts and the other computes the part affinity fields between the body parts. However, the convolution filtering with a large kernel filter takes much time in this model. To accelerate the speed in this model, we propose to change the large kernel filters with binarized multi-scale modules. The large receptive field is captured by the multi-scale structure which also prevents the dropdown of the accuracy in the binarized module. The computation cost and number of parameters becomes small which results in increased speed performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.209-218
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• 2013

Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.