• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.419-420
• /
• 2022

This paper presents a method that allows users to interactively register their own gestures for a motion-based game system. Existing motion-based game systems create recognizers by collecting predefined gesture data. However, this sometimes requires difficult expertise or rather difficult courses. To alleviate these complex situations, we propose a full-body skeleton-based game system that can interactively register gestures.

• International Journal of Contents
• /
• v.14 no.2
• /
• pp.41-44
• /
• 2018

In this paper, we propose a novel method for developing expressive typography authoring tools with personal emotions. Our goal is to implement an interactive typography system that does not rely on any particular language and provides an easy, natural user interface and allows for immediate interaction. For this purpose, we converted the text data entered by a user to image data. The image data was then used for interaction with the user. The data was synchronized with the user's skeleton information obtained from the depth camera. We decomposed the characters using the formality of language to provide a typographical movement that responds more dynamically to the user's motion. Thus, this system provides interaction as a unit of characters rather than as a whole character, allowing the user to have emotional and aesthetic emotional immersion into his or her creation.

• International Journal of Internet, Broadcasting and Communication
• /
• v.12 no.4
• /
• pp.83-92
• /
• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, 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 the conventional primitive skeletal data use method.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.4
• /
• pp.883-898
• /
• 1996

In this paper, we propose improved method to recognize the shape for enhancing the quality of the pattern recognition system by compressing the source images. In the proposed method, we reduced the data amount by skeletonizing the source images using mathematical morphology, and then matched patterns after accomplishing the translation and scale normalization, and rotation invariance on the transformed images. Through the scale normalization, it was possible for the shape recognition at minimum amount of the pixel by giving the weight to the skeleton pixel. As the source images was replaced by the skeleton images, it was possible to reduce the amount of data and computational loads dramatically, and so become much faster even with a smaller memory capacity. Through the experiment, we investigated the optimum scale factor and good result was proved when realizing the pattern recognition system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.929-944
• /
• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.11
• /
• pp.491-500
• /
• 2021

In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypoints and a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning model such as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposed method receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and then recognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder, spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extracted features were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existing studies such as GRU and LSTM was demonstrated through experiments.

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

• Proceedings of the KIEE Conference
• /
• 1998.07c
• /
• pp.1185-1187
• /
• 1998

The object of this study shows a program for supervisory control of the electric power distribution, approach to the more easy manipulations and hangulization that could be easily familiar to the domestic operators whereas the program, up to now, displayed only in English. This program is consist of a skeleton-diagram designer and a state-displayer of the electric power distribution To make skeleton-diagram data available in state-displayer, the designer must be performed with the proposed format in the program manual. States display executes downloading S/Ws-state-data externally and display the S/W informations at that times, and also power distribution simulation of voluntary S/W manipulations performed by the internal operators is possible.

• The korean journal of orthodontics
• /
• v.20 no.2
• /
• pp.391-407
• /
• 1990

This study was performed to establish the cephalometric standards and to observe the nature of anatomic fit in the internal structural relationship of the craniofaciodental complex of the normal Korean by means of Moyers' method. Lateral cephalograms of 143 males and 144 females with normal occlusion and acceptable profile from 6 to 25 years of age, which were consisted of 5 groups that were 6 year-, 9 year-, 12 year-, 15 year- and adult-group were obtained. Data were gathered by traced digitizing the cephalograms and were statistically analyzed. The findings can be summerized as follows. 1. Norms of Korean males, females and both sexes in each group were established. 2. There was little significant sexual dimorphism in the form of craniofacial skeleton of all age groups. 3. The height and length of craniofacial skeleton was alike in each sexes in the 6 year-, 9 year- and 12 year-group, whereas it was larger in male than in female in the 15 year- and adult-group. 4. There were no significant sexual differences in the internal structural relationship of the craniofacial skeleton in all age groups.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.24 no.6
• /
• pp.605-612
• /
• 2015

Three dimensional (3D) position determination and motion recognition using a 3D depth sensor camera are applied to a developed penguin-shaped robot, and its validity and closeness are investigated. The robot is equipped with an Asus Xtion Pro Live as a 3D depth camera, and a sound module. Using the skeleton information from the motion recognition data extracted from the camera, the robot is controlled so as to follow the typical three mode-reactions formed by the operator's gestures. In this study, the extraction of skeleton joint information using the 3D depth camera is introduced, and the tracking performance of the operator's motions is explained.