• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.5-7
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• 2021

Motion capture data files for pose estimation may have inaccurate data depending on the surrounding environment and the degree of movement, so it is necessary to correct it. In the past, inaccurate data was restored with post-processing by people, but recently various kind of neural networks such as LSTM and R-CNN are used as automated method. However, since neural network-based data restoration methods require a lot of computing resource, this paper proposes a method that reduces computing resource and maintains data restoration rate compared to neural network-based method. The proposed method automatically restores inaccurate motion capture data by using posture measurement data (c3d). As a result of the experiment, data restoration rates ranged from 89% to 99% depending on the degree of inaccuracy of the data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.607-616
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• 2014

Face recognition is a big challenge in surveillance system since different rotation angles of the face make the difficulty to recognize the face of the same person. This paper proposes a novel method to recognize face with different head poses by using 3D information of the face. Firstly, head pose estimation (estimation of different head pose angles) is accomplished by the POSIT algorithm. Then, 3D face image data is constructed by using head pose estimation. After that, 2D image and the constructed 3D face matching is performed. Face verification is accomplished by using commercial face recognition SDK. Performance evaluation of the proposed method indicates that the error range of head pose estimation is below 10 degree and the matching rate is about 95%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.127-133
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• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.939-940
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• 2010

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.24-34
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• 2007

Application systems that easy to access a information have been developed by IT growth and a human life variation. In this paper, we propose a application system to register a 3D footwear model using a monocular camera. In General, a human motion analysis research to body movement. However, this system research a new method to use a foot movement. This paper present a system process and show experiment results. For projection to 2D foot plane from 3D shoe model data, we construct processes that a foot tracking, a projection expression and pose estimation process. This system divide from a 2D image analysis and a 3D pose estimation. First, for a foot tracking, we propose a method that find fixing point by a foot characteristic, and propose a geometric expression to relate 2D coordinate and 3D coordinate to use a monocular camera without a camera calibration. We make a application system, and measure distance error. Then, we confirmed a registration very well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.285-291
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• 2020

In manufacturing, humans are being replaced with robots, but expert skills remain difficult to convert to data, making them difficult to apply to industrial robots. One method is by visual motion recognition, but physical features may be judged differently depending on the image data. This study aimed to improve the accuracy of vision methods for estimating the posture of humans. Three OpenPose vision models were applied: MPII, COCO, and COCO+foot. To identify the effects of face-covering accessories and image preprocessing on the Convolutional Neural Network (CNN) structure, the presence/non-presence of accessories, image size, and filtering were set as the parameters affecting the identification of a human's posture. For each parameter, image data were applied to the three models, and the errors between the actual and predicted values, as well as the percentage correct keypoints (PCK), were calculated. The COCO+foot model showed the lowest sensitivity to all three parameters. A <50% (from 3024×4032 to 1512×2016 pixels) reduction in image size was considered acceptable. Emboss filtering, in combination with MPII, provided the best results (reduced error of <60 pixels).

• Journal of the Korean Society of Costume
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• v.42
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• pp.69-90
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• 1999

Mannerism style evolved from the Renaissance style adopting the concept of grace as the ideal beauty, Having its ground on Neoplatonism the main goal of mannerism art was the realization of the invisible beauty over reality. Mannerism style in dress flourished in the sixteenth century court society, when courtly manners and courtly grace became the most important qualities in social relationship. Courtiers thought that courtly grace the ideal of beauty could be realized in the cultured and studied elegance. Mannerism style in dress evolved from the process of transforming and manipulating the Renaissance look for the abstract of beauty. The clothes of Mannerism style were against the natural movement of the human body. There was a tendency of refining and polishing the whole clothing and various technical skills were experim-ented on the mannerism style. The outstanding elements of this tendency can be found in the details like ruffs fathingale padding slashing puffing and etc. Mannerism intended to reconstruct the human body artificially to express courtly grace and novelty. During that process the new pose 'figura serpentinata' which is bizarre convoluted pose with full of flexibility was created. The expression of human body became more slender with elongated legs a torso with a long neck and a tiny head. This tendency of distorting the natural body forms were reflected in the formal characteristics of Mannerism dress style which is geometrical abstr-action unnatural elongation complex disposition and control with perfect ease.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.264-272
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• 2019

In this paper, we propose a 3D human body reconstruction and refinement method from the frames extracted from a video to obtain natural and smooth motion in temporal domain. Individual frames extracted from the video are fed into convolutional neural network to estimate the location of the joint and the silhouette of the human body. This is done by projecting the parameter-based 3D deformable model to 2D image and by estimating the value of the optimal parameters. If the reconstruction process for each frame is performed independently, temporal consistency of human pose and shape cannot be guaranteed, yielding an inaccurate result. To alleviate this problem, the proposed method analyzes and interpolates the principal component parameters of the 3D morphable model reconstructed from each individual frame. Experimental result shows that the erroneous frames are corrected and refined by utilizing the relation between the previous and the next frames to obtain the improved 3D human reconstruction result.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.113-119
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• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.