• 대한임베디드공학회논문지
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• 제17권4호
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• pp.191-197
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• 2022

This study proposes an image-based Pose Intention Network (PIN) algorithm for rehabilitation via patients' intentions. The purpose of the PIN algorithm is for enabling an active rehabilitation exercise, which is implemented by estimating the patient's motion and classifying the intention. Existing rehabilitation involves the inconvenience of attaching a sensor directly to the patient's skin. In addition, the rehabilitation device moves the patient, which is a passive rehabilitation method. Our algorithm consists of two steps. First, we estimate the user's joint position through the OpenPose algorithm, which is efficient in estimating 2D human pose in an image. Second, an intention classifier is constructed for classifying the motions into three categories, and a sequence of images including joint information is used as input. The intention network also learns correlations between joints and changes in joints over a short period of time, which can be easily used to determine the intention of the motion. To implement the proposed algorithm and conduct real-world experiments, we collected our own dataset, which is composed of videos of three classes. The network is trained using short segment clips of the video. Experimental results demonstrate that the proposed algorithm is effective for classifying intentions based on a short video clip.

• 로봇학회논문지
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• 제14권2호
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• pp.104-113
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• 2019

Object manipulation in cluttered environments remains an open hard problem. In cluttered environments, grasping objects often fails for various reasons. This paper proposes a novel task and motion planning scheme to grasp objects obstructed by other objects in cluttered environments. Task and motion planning (TAMP) aims to generate a sequence of task-level actions where its feasibility is verified in the motion space. The proposed scheme contains an open-loop consisting of three distinct phases: 1) Generation of a task-level skeleton plan with pose references, 2) Instantiation of pose references by motion-level search, and 3) Re-planning task based on the updated state description. By conducting experiments with simulated robots, we show the high efficiency of our scheme.

• 한국멀티미디어학회논문지
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• 제24권2호
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• pp.336-343
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• 2021

AI technology is spreading not only to industrial fields, but also to culture, art, and content fields. In this paper, we proposed a system based on AI technology that can automate the process of reproducing contents using characters for movie contents. After creating the basic appearance of the character by using the StyleGAN2 model from the video extracted from the movie contents, analyzing the character's personality and propensity using the extracted dialogue data, it was determined from the contemplative appearance based on the yin-yang and five elements to the character's propensity. Accordingly, the external characteristics are reflected in the character. Using the OpenPose model, a character's motion is created, and the finally generated data is integrated to reproduce the content. It is expected that many movie contents can be reproduced through the study of the proposed system.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.1056-1059
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• 2020

최근 드론과 1인 미디어 시장의 성장으로, 영상 촬영 분야에서의 드론 산업이 활발하게 발전되고 있다. 본 논문에서는 딥러닝 기반 다중 객체 인식 기술인 Openpose를 활용하여 인물촬영을 위한 음성인식 드론 제어 시스템을 제안한다. 해당 시스템은 자연어 처리된 음성명령어를 통해 드론이 각 촬영 객체에 대한 회전, 초점변화 등 실제 영상촬영기법에 사용되는 다수의 동작을 수행할 수 있도록 한다. 최종적으로 96.2%의 정확도로 음성명령에 따라 동작을 수행하는 것을 확인할 수 있다. 이는 누구나 전문적 지식이나 경험 없이 음성만으로 쉽게 드론을 제어할 수 있을 것으로 기대된다.

• 대한수학회논문집
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• 제11권4호
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• pp.1067-1075
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• 1996

We show that every ultrapure space is weakly star reducible, and that every countably compact weakly star reducible space is compact. We also pose open problems.

• 한국멀티미디어학회논문지
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• 제22권5호
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• pp.535-546
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• 2019

Recently, there have been a lot of researches on the whole face replacement system, but it is not easy to obtain stable results due to various attitudes, angles and facial diversity. To produce a natural synthesis result when replacing the face shown in the video image, technologies such as face area detection, feature extraction, face alignment, face area segmentation, 3D attitude adjustment and facial transposition should all operate at a precise level. And each technology must be able to be interdependently combined. The results of our analysis show that the difficulty of implementing the technology and contribution to the system in facial replacement technology has increased in facial feature point extraction and facial alignment technology. On the other hand, the difficulty of the facial transposition technique and the three-dimensional posture adjustment technique were low, but showed the need for development. In this paper, we propose four facial replacement models such as 2-D Faceswap, OpenPose, Deekfake, and Cycle GAN, which are suitable for the Cox platform. These models have the following features; i.e. these models include a suitable model for front face pose image conversion, face pose image with active body movement, and face movement with right and left side by 15 degrees, Generative Adversarial Network.

• 한국CDE학회논문집
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• 제16권4호
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• pp.285-291
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• 2011

A pose estimation process from medical images is calculating locations and orientations of objects obtained from Computed Tomography (CT) volume data utilizing X-ray images from two directions. In this process, digitally reconstructed radiograph (DRR) images of spatially transformed objects are generated and compared to X-ray images repeatedly until reasonable transformation matrices of the objects are found. The DRR generation and image comparison take majority of the total time for this pose estimation. In this paper, a fast DRR generation technique based on GPU parallel computing is introduced. A volume ray-casting algorithm is explained with brief vector operations and a parallelization technique of the algorithm using Compute Unified Device Architecture (CUDA) is discussed. This paper also presents the implementation results and time measurements comparing to those from pure-CPU implementation and open source toolkit.

• 한국재난정보학회 논문집
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• 제19권3호
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• pp.498-509
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• 2023

연구목적: 본 논문은 CCTV 영상을 활용한 딥러닝 객체 인식 기술을 적용해 지하공동구 내 쓰러진 관리인력의 검출 방법을 제시하고, 제안 방법의 관리인력 모니터링 적용성을 평가한다. 연구방법: 사람 검출 목적으로 사전 훈련된 YOLOv5와 OpenPose 모델의 추론 결과로부터 쓰러짐을 판별할 수 있는 규칙을 제안하고, 각 모델의 결과를 통합해 지하공동구 내 작업자 쓰러짐 검출에 적용하였다. 연구결과: 제안된 모델로 작업인력의 감지 및 쓰러짐을 판단할 수 있었으나, CCTV와 작업자 간격 및 작업자가 쓰러진 방향에 의존해 검출성능이 영향을 받았다. 또한 지하공동구 작업자에 대해 YOLOv5 기반 쓰러짐 판별 규칙 적용 모델이 거리 및 쓰러짐 방향 의존성이 낮아 OpenPose 기반 모델에 비해 우수한 성능을 보였다. 그 결과 통합된 이중 딥러닝 모델의 쓰러짐 검출 결과는 YOLOv5 결과에 종속되었다. 결론: 제안 모델을 통해 지하공동구 작업자의 이상상황 검출이 가능함을 보였으나, 개별 딥러닝 모델별 사람 감지 성능 차이로 인해 YOLOv5 기반 모델 대비 통합 모델의 쓰러짐 검출 성능 개선은 미미하였다.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.303-310
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• 2023

A single vision system limits the ability to accurately understand the spatial constraints and interactions between robots and dynamic workers caused by gantry robots and collaborative robots during production manufacturing. In this paper, we propose a 3D pose registration method for dynamic workers based on a multi-domain vision system for safety monitoring in manufacturing environments. This method uses OpenPose, a deep learning-based posture estimation model, to estimate the worker's dynamic two-dimensional posture in real-time and reconstruct it into three-dimensional coordinates. The 3D coordinates of the reconstructed multi-domain vision system were aligned using the ICP algorithm and then registered to a single 3D coordinate system. The proposed method showed effective performance in a manufacturing process environment with an average registration error of 0.0664 m and an average frame rate of 14.597 per second.