• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.224-229
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• 2003

In this paper, an effective skeleton method is proposed in order to obtain an enhanced digital image of skeleton line. The edge-detection method is applied in the preprocessing stage and after that, the modified Parallel method is applied to obtain the improved image of skeleton line. The existing parallel methods are Zhang, Lu and Wang, and Paul methods. Firstly, a parallel process method Is applied, and the proposed method is applied that the original is compared with the four neighbor pixels and four corner pixels of mask. In conclusion, the proposed method shows an improved connectivity and quality of skeleton line.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.731-736
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• 2008

In this paper, we propose the movement detection algorithm by using virtual skeleton model. To do this, first, we eliminate error values by using conventioanl method based on RGB color model and eliminate unnecessary values by using the HSI color model. Second, we construct the virtual skeleton model with skeleton information of 10 peoples. After matching this virtual model to original image, we extract the real head silhouette by using the proposed circle searching method. Third, we extract the object by using the mean-shift algorithm and this head information. Finally, we validate the applicability of the proposed method through the various experiments in a complex environments.

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

• International Journal of Contents
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• v.13 no.1
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• pp.68-75
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• 2017

Interactive Typography is a process where a user communicates by interacting with text and a moving factor. This research covers interactive typography using real-time response to a user's gesture. In order to form a language-independent system, preprocessing of entered text data presents image data. This preprocessing is followed by recognizing the image data and the setting interaction points. This is done using computer vision technology such as the Harris corner detector and contour detection. User interaction is achieved using skeleton information tracked by a depth camera. By synchronizing the user's skeleton information acquired by Kinect (a depth camera,) and the typography components (interaction points), all user gestures are linked with the typography in real time. An experiment was conducted, in both English and Korean, where users showed an 81% satisfaction level using an interactive typography system where text components showed discrete movements in accordance with the users' gestures. Through this experiment, it was possible to ascertain that sensibility varied depending on the size and the speed of the text and interactive alteration. The results show that interactive typography can potentially be an accurate communication tool, and not merely a uniform text transmission system.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.491-500
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• 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
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• v.25 no.5
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• pp.808-811
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• 2020

This paper proposes a method to minimize the error of the Kinect camera sensor by using a random undirected Kalman filter. Kinect cameras, which provide RGB values and depth information, cause nonlinear errors in the sensor, causing problems in various applications such as skeleton detection. Conventional methods have tried to remove errors by using various filtering techniques. However, there is a limit to removing nonlinear noise effectively. Therefore, in this paper, a randomized unscented Kalman filter was applied to predict and update the nonlinear noise characteristics, we next tried to enhance a performance of skeleton detection. The experimental results confirmed that the proposed method is superior to the conventional method in quantitative results and reconstructed images on 3D space.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.586-594
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• 2018

In this paper, we propose a livestock theft detection system through moving object classification and tracking method. To do this, first, we extract moving objects using GMM(Gaussian Mixture Model) and RGB background modeling method. Second, it utilizes a morphology technique to remove shadows and noise, and recognizes moving objects through labeling. Third, the recognized moving objects are classified into human and livestock using skeletal features and color similarity judgment. Fourth, for the classified moving objects, CAM (Continuously Adaptive Meanshift) Shift and Kalman Filter are used to perform tracking and overlapping judgment, and risk is judged to generate a notification. Finally, several experiments demonstrate the feasibility and applicability of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.4
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• pp.451-458
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• 2015

This paper handles self-collision avoidance for a rescue robot with redundant manipulators. In order to detect all available self-collisions in advance, minimum distances between arbitrary robot parts should be monitored in real-time. For the minimum distance estimation, we suggest a modified method from a previous skeleton algorithm which has less computation burden and realize collision avoidance based on a potential function using the proposed algorithm. The resultant command by collision avoidance should not disturb a given primary task, so null-space of joint solution from a CLIK is utilized for collision avoidance by a gradient projection method.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.