• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.875-880
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• 2013

When we generate 3D video that provides immersive and realistic feeling to users, depth information of the scene is essential. Since the resolution of the depth map captured by a depth sensor is lower than of the color image, we need to upsample the low-resolution depth map for high-resolution 3D video generation. In this paper, we propose a depth upsampling method using depth-discontinuity information. Using the high-resolution color image and the low-resolution depth map, we detect depth-discontinuity regions. Then, we define an energy function for the depth map upsampling and optimize it using the belief propagation method. Experimental results show that the proposed method outperforms other depth upsampling methods in terms of the bad pixel rate.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.33-43
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• 2011

Recently, 3D displays are supplied in the 3D markets so that the demand for 3D stereoscopic contents increases. In general, a simple method is to use a stereoscopic camera. As well, the production of 3D from 2D materials is regarded as an important technology. Such conversion works have gained much interest in the field of 3D converting. However, the stereoscopic image generation from a single 2D image is limited to simple 2D to 3D conversion so that the better realistic perception is difficult to deliver to the users. This paper presents a new stereoscopic content production method where foreground objects undergo alive action events. Further stereoscopic animation is viewed on 3D displays. Given a 2D image, the production is composed of background image generation, foreground object extraction, object/background depth maps and stereoscopic image generation The alive objects are made using the geometric transformation (e.g., translation, rotation, scaling, etc). The proposed method is performed on a Korean traditional painting, Danopungjung as well as Pixar's Up. The animated video showed that through the utilization of simple object transformations, more realistic perception can be delivered to the viewers.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.779-785
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• 2022

Study analyzes whether the 3D CAD class using the flipped learning-based PBL is effective in acquiring professional knowledge and nurturing talent. A Flipped Learning-based PBL class was implemented for 3rd grade students of Robot and Automation Engineering Major, Dong-Eui University, and a survey was conducted on satisfaction and effectiveness. The students seemed to be generally satisfied with the class, and the flipped learning-based PBL appeared to be effective in improving the competency required by companies. In particular, it is hoped that it will contribute to the use of video education in practical subjects in the future by proving that practical classes can be operated effectively even in non-face-to-face learning. Moreover, this study is an important indicator for future research and will be used as a quantitative indicator for class improvement.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.42-45
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• 2010

This paper proposes an intelligent video surveillance system for human object tracking. The proposed system integrates the object extraction, human object recognition, face detection, and camera control. First, the object in the video signals is extracted using the background subtraction. Then, the object region is examined whether it is human or not. For this recognition, the region-based shape descriptor, angular radial transform (ART) in MPEG-7, is used to learn and train the shapes of human bodies. When it is decided that the object is human or something to be investigated, the face region is detected. Finally, the face or object region is tracked in the video, and the pan/tilt/zoom (PTZ) controllable camera tracks the moving object with the motion information of the object. This paper performs the simulation with the real CCTV cameras and their communication protocol. According to the experiments, the proposed system is able to track the moving object(human) automatically not only in the image domain but also in the real 3-D space. The proposed system reduces the human supervisors and improves the surveillance efficiency with the computer vision techniques.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.66-69
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• 2000

In this paper, we propose a progressive transmission of a video using a 2$\times$2$\times$2 DCT First of all, the video data is transformed into multiresolution represented video data using a 2$\times$2$\times$2 DCT. Then. it is represented by a 3-D EZT(Embedded Zero Tree) coding fur the progressive transmission with a bit-rate scalability. The proposed progressive transmission algorithm needs much less computations and buffer memories than the higher-order convolution based wavelet filter. Also, since the 2$\times$2$\times$2 DCT requires independent local computations, parallel processing can be applied.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.624-629
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• 2007

While single view video coding uses the temporal prediction scheme, multi-view video coding (MVC) applies both temporal and inter-view prediction schemes. Thus, the key problem of MVC is how to reduce the inter-view redundancy efficiently, because various existing video coding schemes have already provided solutions to reduce the temporal correlation. In this paper, we propose a global disparity compensation scheme which increases the inter-view correlation and a new inter-view prediction structure based on the global disparity compensation. By experiment, we demonstrate that the proposed global disparity compensation scheme is less sensitive to change of the search range. In addition, the new Inter-view prediction structure achieved about $0.1{\sim}0.3dB$ quality improvement compared to the reference software.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.150-161
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• 2018

We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.780-784
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• 2011

As the popularization and development of 3D display makes common users easy to experience a solid 3D virtual reality, the demand for virtual reality contents are increasing. In this paper, we propose 3D panorama system using vanishing point locationbased depth map generation method. 3D panorama using depthmap stitching gives an effect that makes users feel staying at real place and looking around nearby circumstances. Also, 3D panorama gives free sight point for both nearby object and remote one and provides solid 3D video.