• Journal of Broadcast Engineering
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• v.3 no.2
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• pp.127-137
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• 1998

In this paper, we propose the method of extracting edge information from MPEG video sequences for the detection of scene changes. In a the proposed method, five significant AC coefficients of each MPEG block are utilized to obtain edge images from the MPEG video. AC edge images obtained by the proposed scheme not only produce better object boundary information than conventional methods using only DC coefficients, but also can reduce the boundary effects produced by DC-based. Since the AC edge image contains the content information of each frame, it can be effectively utilized for the detection of scene change as well as the content-based video query. Experimental results show that the proposed method can be effectively utilized for the detection of scene changes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1690-1696
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• 1999

Video sequence should be hierachically classified for the content-based retrieval. Cut detection algorithm is an essential process to classify shots. It is generally difficult for cut detection algorithms to detect cut points since a current frame is compared with a previous one, because movement of camera or object made adrupt scene change. We reduce ratio of failed cut detection so that compare the difference between frames of predicted cut point and their neighbors. In this paper, first we get predicted cut point, then we judge that the predicted cut point is true point or not. And we extracted DC images in MPEG video sequence for comparison. As a result of experiments. We confirmed that the cut detection ratio of the proposed algorithm is higher than of any other algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06b
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• pp.83-87
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• 1996

A virtual reality system using video image is designed and implemented. A participant having 2{{{{ { 1} over { 2} }}}}DOF can interact with the computer-generated virtual object using her/his full body posture and gesture in the 3D virtual environment. The system extracts the necessary participant-related information by video-based sensing, and simulates the realistic interaction such as collision detection in the virtual environment. The resulting scene obtained by compositing video image of the participant and virtual environment is updated in near real time.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.778-786
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• 2002

Scene boundary detection is important in the understanding of semantic structure from video data. However, it is more difficult than shot change detection because scene boundary detection needs to understand semantics in video data well. In this paper, we propose a new approach to scene segmentation using contextual information in video data. The contextual information is divided into two categories: local and global contextual information. The local contextual information refers to the foreground regions' information, background and shot activity. The global contextual information refers to the video shot's environment or its relationship with other video shots. Coherence, interaction and the tempo of video shots are computed as global contextual information. Using the proposed contextual information, we detect scene boundaries. Our proposed approach consists of three consecutive steps: linking, verification, and adjusting. We experimented the proposed approach using TV dramas and movies. The detection accuracy of correct scene boundaries is over than 80%.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• Smart Media Journal
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• v.5 no.1
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• pp.78-87
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• 2016

The major problems of recent object tracking methods are related to the inefficient detection of moving objects due to occlusions, noisy background and inconsistent body motion. This paper presents a robust method for the detection and tracking of a moving in infrared animal videos. The tracking system is based on adaptive optical flow generation, Gaussian mixture and Kalman filtering. The adaptive Gaussian model of optical flow (GMOF) is used to extract foreground and noises are removed based on the object motion. Kalman filter enables the prediction of the object position in the presence of partial occlusions, and changes the size of the animal detected automatically along the image sequence. The presented method is evaluated in various environments of unstable background because of winds, and illuminations changes. The results show that our approach is more robust to background noises and performs better than previous methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.521-527
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• 2009

This paper suggests a collision techniques for a higher reality in augmented reality by processing collision between a real object obtained through video frame input and a marker-based virtual object or a virtual object from Opengl. Augmented reality system is providing the visual information containing a virtual object added to the real environment and interactive interface between objects and between user and objects becomes a more concerning interest. But the collision problem is essential to the interactive interface and has to be solved first. Therefore, the proposed system suggests a solution for it to increase the realism and the immersion by validating the collision among a marker-based object, a virtual object from Opengl, and a real object obtained through web camera, that is, video frame.

• The Journal of the Korea Contents Association
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• v.2 no.2
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• pp.17-26
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• 2002

In this paper, we suggest a method to acquire the moving object centered video by panning and tilting a camera automatically according to motion vectors calculated by detecting the motion of a moving object on video steam. We create a difference image by estimating the intensity difference at the grid points of neighboring frames. And we detect the motion using both horizontal projection histogram and vertical projection histogram and decide the center of motion part. Then we calculate a new direction and degree of the motion by comparing coordinates at the center of current motion and the center of previous motion. By controling the RCM using these Motion vectors, we can get video stream positioned unwire object on the center of video frame. Through the experiments, we could get a moving object centered video stream continuously arid monitor remotely by implementing sever/client architecture based on the web.

• Journal of IKEEE
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• v.14 no.4
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• pp.317-323
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• 2010

The video surveillance system is adopted in many places due to its efficiency and constancy in monitoring a specific area over a long period of time. However, many surveillance systems composed of a single static camera often produce unsatisfactory results due to their lack of field of view. In this paper, we present a video surveillance system based on wide baseline stereo cameras to overcome the limitation. We adopt the codebook algorithm and mathematical morphology to robustly model the foreground pixels of the moving object in the scene and calculate the trajectory of the moving object via 3D reconstruction. The experimental results show that the proposed system detects a moving object and generates a top view trajectory successfully to track the location of the object in the world coordinates.