• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.686-689
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• 2018

3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.580-585
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• 2010

In this paper, The object Tracking System is designed by SOPC based Nios II embedded processor and C2H compiler. And this system using single PTZ camera can effectively control IPs in the platform of SOPC based Nios II Embedded Processor and creating IP by C2H(C-To-Hardware) compiler for image-in/output, image-processing and devices of communication that can supply various monitoring information to network or serial. Accordingly, Special quality and processing speed of object tracking using high-quality algorism in the system is improved by hardware/software programming methods.

• Journal of KIBIM
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• v.11 no.3
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• pp.45-54
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• 2021

The augmented reality (AR) has recently became an attractive technology in construction industry, which can play a critical role in realizing smart construction concepts. The AR has a great potential to help construction workers access digitalized information about design and construction more flexibly and efficiently. Though several AR applications have been introduced for on-site made to enhance on-site and off-site tasks, few are utilized in actual construction fields. This paper proposes a new AR framework that provides on-site managers with an opportunity to easily access the information about construction resources such as workers and equipment. The framework records videos with the camera installed on a wearable AR device and streams the video in a server equipped with high-performance processors, which runs an object detection algorithm on the streamed video in real time. The detection results are sent back to the AR device so that menu buttons are visualized on the detected objects in the user's view. A user is allowed to access the information about a worker or equipment appeared in one's view, by touching the menu button visualized on the resource. This paper details implementing parts of the framework, which requires the data transmission between the AR device and the server. It also discusses thoroughly about accompanied issues and the feasibility of the proposed framework.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1330-1336
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• 2021

Media sharing platform such as YouTube have grown significantly in the mobile environment. This is a platform that allows users to select and view broadcast programs that are only available on TV through network-connected PCs and mobiles, and share their media content to communicate. Currently, in the era where mobile and TV broadcasts can be viewed equally, there is a time difference between the video and real-time screen transmitted to TV and mobile, different from the actual situation.We want this time difference to be realized in the same way as the real time, and there is a need for a system that can broadcast in a free environment at any time. Therefore, in this paper, a broadcasting system was designed and implemented in a mobile environment. The result of reducing the delay time difference due to the improvement of the processing method was obtained.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.1
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• pp.1-6
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• 2021

Recently, incidents such as proxy surgery (unlicensed medical practice) have been reported in the media that threaten the safety of patients. Alternatives such as the introduction of operating room surveillance camera devices to prevent proxy surgery are emerging, but there are practical difficulties in implementing them due to strong opposition from the medical community. However, the social credibility of doctors is falling as incidents such as proxy surgery occur frequently. In this paper, we propose a medical staff identification system combining Beacon and iris recognition. The system adds reliability by operating on the blockchain network. The system performs primary identification by performing user authentication through iris recognition and proves that the medical staff is in the operating room through beacons. It also ensures patient trust in the surgeon by receiving beacon signals in the background and performing iris authentication at random intervals to prevent medical staff from leaving the operating room after only performing initial certification.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1053-1059
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• 2021

In order to realize a smart home, a new type of service that converges the two technologies is required as a method to overcome the respective limitations of augmented reality and IoT technologies. Augmented reality recognizes objects and projects augmented content with the recognized objects on the screen. This technology mainly uses image processing methods such as markers as a method for recognizing objects. In this paper, an augmented reality-based IoT control platform using markers was developed. By defining a marker unique to the object, a unique identifier displayed on the camera was distinguished. A smart home system was implemented by calling a controller to control things. The proposed system receives state information of objects through symptom reality and transmits control commands. The proposed platform was verified by manipulating household lights.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.39-44
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• 2022

The purpose of this study is to extract the type, location, and absolute size of an object in an image using a deep learning algorithm, predict the relative distance between objects, and use this to detect contact between objects. To analyze the size ratio of objects, YOLO, a CNN-based object detection algorithm, is used. Through the YOLO algorithm, the absolute size and position of an object are extracted in the form of coordinates. The extraction result extracts the ratio between the size in the image and the actual size from the standard object-size list having the same object name and size stored in advance, and predicts the relative distance between the camera and the object in the image. Based on the predicted value, it detects whether the objects are in contact.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.519-525
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• 2022

The face detection task of detecting a person's face in an image is used as a preprocess or core process in various image processing-based applications. The neural network models, which have recently been performing well with the development of deep learning, are dependent on 2D images, so if noise occurs in the image, such as poor camera quality or pool focus of the face, the face may not be detected properly. In this paper, we propose a face detection method that uses depth information together to reduce the dependence of 2D images. The proposed model was trained after generating and preprocessing depth information in advance using face detection dataset, and as a result, it was confirmed that the FRN model was 89.16%, which was about 1.2% better than the RetinaNet model, which showed 87.95%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.