• Korean Journal of Applied Biomechanics
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• v.13 no.1
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• pp.185-204
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• 2003

The purpose of this study was to obtain the data for stable and accurate techniques of the free throw in basketball. The subjects of this study were seven male basketball player consisted of college students athletes. Free throw motions were taken by video camera. The three-dimensional coordinates was processed by DLT. The variables were the velocity, the angular velocity of the upper extremity segments, degree, and angular momentum. The result of analysis is summarized as follows. 1. The velocity and angular velocity of the upper extremity segment was showed an gradual increase and a smooth velocity transfer, transferring from proximal segment to distal segment at free throw motion in basketball. 2. The local term and remote term angular velocity momentum of the proximal segment showed larger than that of the distal segment in X, Y, Z axis component all. 3. The remote term angular momentum was showed larger than that of the local term angular momentum in X, Y, Z axis component all. 4. The angular motion of the upper trunk and upper arm, upper arm and forearm was showed in opposite direction and symmetrical angular momentum in local term angular momentum of the Y and Z axis component. 5. All the segments of upper extremity segment was showed left rotation in remote term angular momentum of the Y axis component and right rotation in remote term angular momentum of the Z axis component.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.749-751
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• 2016

Due to the advent of virtual reality, the past communication method using images and video through the expansion into three-dimensional space has been provided more realistic and seamless interaction environment. Unlike reality, virtual reality is under a full human control and due to this benefit can be used as a substitute for reality therefore medicine and healthcare area has attracted attention in the prevention and treatment of dementia utilizing virtual reality. The research provided in this paper is aimed to design a virtual reality-based mild cognitive impairment prevention training system, focusing on the Symptoms of Alzheimer's precursor, mild cognitive impairment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Cartoon and Animation Studies
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• s.9
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• pp.1-17
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• 2005

The image that reflects the lapse of time can only take the form of segmentation originally. All of the fixation status in an image is shown to us as the form of division(segmentation) whether it is the form of two dimensional paintings or three dimensional sculptures, but not the form of video monitors. This kind of method was extremely developed in the cartoon field. Therefore, the cartoon evolves the subjects of events and finds the worth and meaning through the arrangement of cuts. Also, Animation inquires into the principles of attaching the media continuously. The media is represented in the segmentation according to the lapse of time and I tend to analyze the origination of cartoon formation. Consequently, I tired to find the examples of the prehistoric cuts and forms, the divisions of fragmented cartoon stripes, and the cuts and forms which are composed of narrative and descriptive styles of arrangement, and finally can certify and validate them. In summary, the sections and divisions of the variable kinds of cuts and frames for the expression of the time were corroborated. The potentials of the space usage and the diversity of the cartoon formation were also founded and illustrated to suggest the broader stands of cartoon.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.31-42
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• 2019

With the recent development of deep learning technology, remarkable achievements have been made in many research areas of computer vision. Deep learning has also made dramatic improvement in two-dimensional or three-dimensional human pose estimation based on a single image, and many researchers have been expanding the scope of this problem. The human pose estimation is one of the most important research fields because there are various applications, especially it is a key factor in understanding the behavior, state, and intention of people in image or video analysis. Based on this background, this paper surveys research trends in estimating human poses based on a single image. Because there are various research results for robust and accurate human pose estimation, this paper introduces them in two separated subsections: 2D human pose estimation and 3D human pose estimation. Moreover, this paper summarizes famous data sets used in this field and introduces various studies which utilize human poses to solve their own problem.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• 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 Broadcast Engineering
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• v.16 no.5
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• pp.750-761
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• 2011

Currently, 3DTV technologies are being developed as the future services of the HD digital broadcast environment. As one of the various research topics to apply 3DTV technologies to the conventional broadcasting network, methods to configure stereoscopic videos are being studied. In this paper, we proposed a method to broadcast high quality stereoscopic videos based on analysis of a method to add a stereoscopic descriptor to the PMT of MPEG-2 transport streams and a method to transmit stereoscopic videos by the expansion of MPEG-C part 3 which are from precedent studies. The proposed technique maintains compatibility with conventional MPEG-2 transport streams by showing only reference video for models that do not support 3D broadcasting. Therefore, the compatibility between conventional broadcasting and stereoscopic videos should make this method useful when activating 3D services in the communications and broadcasting area