• Korean Journal of Applied Biomechanics
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• v.17 no.2
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• pp.167-176
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• 2007

The purpose of this study was to offer suitable model for performing Tippelt motion and data for training Tippelt motion through the quantitative kinematical analysis of Tippelt motion in parallel bars. The results of analysing kinematic variations through three-dimensional reflection analysis of three members of the national team as the objects of the study were shown as follows. 1. It seemed that the shoulder-joints which are stretched as much as possible affects the whole Tippelt motion while one is swinging downward. The time of process of the center of mass for the body reaching to the maximum flection point should be quick and body's moving from the vertical phase to the front direction should be controled as much as possible. 2. While one is swinging upward, the stability of flying motion could be made certain by the control of body's rapid moving to the front direction and stretching shoulder-joints and hip-joint to reverse direction. 3. While one is flying upward, the body should be erected quickly and lessening the angle of the hip-joint affects the elevation of flight. When the powerful counter turn motion is performed, the stable motion could be made. As a result of this study, It seems that sudden fall and the maximum stretch of shoulder-joints is important during performing Tippelt motion in parallel bars. Also, it concludes that the maximum bending of hip-joints at the starting point of upward swing, sudden stretch to the reverse direction of shoulder-joints and hip-joints when one is leaving bars, control of body's moving to the front direction, and lessening the angle of hip-joints at the flying phase is important.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.27-34
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• 2013

Background subtraction is one of the key techniques for automatic video content analysis, especially in the tasks of visual detection and tracking of moving object. In this paper, we present a new sample-based technique for background extraction that provides background image as well as background model. To handle both high-frequency and low-frequency events at the same time, multiple interval background models are adopted. The main innovation concerns the use of a confidence factor to select the best model from the multiple interval background models. To our knowledge, it is the first time that a confidence factor is used for merging several background models in the field of background extraction. Experimental results revealed that our approach based on multiple interval sampling works well in complicated situations containing various speed moving objects with environmental changes.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.181-192
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• 2014

In this paper, we propose a robust real-time myocardial border tracking algorithm for echocardiography. Commonly, after an initial contour of LV border is traced at one or two frame from the entire cardiac cycle, LV contour tracking is performed over the remaining frames. Among a variety of tracking techniques, optical flow method is the most widely used for motion estimation of moving objects. However, when echocardiography data is heavily corrupted in some local regions, the errors bring the tracking point out of the endocardial border, resulting in distorted LV contours. This shape distortion often occurs in practice since the data acquisition is affected by ultrasound artifacts, dropout or shadowing phenomena of cardiac walls. The proposed method deals with this shape distortion problem and reflects the motion realistic LV shape by applying global deformation modeled as affine transform partitively to the contour. We partition the tracking points on the contour into a few groups and determine each affine transform governing the motion of the partitioned contour points. To compute the coefficients of each affine transform, we use the least squares method with equality constraints that are given by the relationship between the coefficients and a few contour points showing good tracking results. Many real experiments show that the proposed method supports better performance than existing methods.

• Journal of KIISE:Databases
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• v.31 no.3
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• pp.205-218
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• 2004

As the development of database technology for managing spatiotemporal data, new types of spatiotemporal application services that need the spatiotemporal knowledge discovery from the large volume of spatiotemporal data are emerging. In this paper, a new 3-layered discovery framework for the development of spatiotemporal knowledge discovery techniques is proposed. The framework supports the foundation model in order not only to define spatiotemporal knowledge discovery problem but also to represent the definition of spatiotemporal knowledge and their relationships. Also the components of spatiotemporal knowledge discovery system and its implementation model are proposed. The discovery framework proposed in this paper satisfies the requirement of the development of new types of spatiotemporal knowledge discovery techniques. The proposed framework can support the representation model of each element and relationships between objects of the spatiotemporal data set, information and knowledge. Hence in designing of the new types of knowledge discovery such as spatiotemporal moving pattern, the proposed framework can not only formalize but also simplify the discovery problems.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.25-32
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• 2016

In this paper, we propose a method that is tracking an object in real time using particle filter and the observation model with edge. First of all, the proposed method defines the object to be tracked in the initial frame. Then, it generates the edge observation model for the object to be tracked and a set of particles. It calculates the weight by comparing the average of the middle distance in eight-way of particle filter edge model with that in edge observation model, and then updates the weight with the calculated value. After resampling particles using the updated weights, it estimates the current location of the tracked object. Finally, this paper demonstrates the performance of the stable tracking through comparison with the existing method by using a number of experimental data.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.435-442
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• 2009

We propose an annotation system, which allows users moving on an environment to receive personalized messages that are generated by exploiting contextual information. In the system, the context is defined as an entity including user's identity, location and time. Identity of user is a key data to enable personal aspect of generated message. For sensing the context, the proposed system uses AR(augmented reality) technology. Markers are attached to real objects for tracking user's location. AR can provide an effective annotating method to enhance human's perception and interaction abilities. The received message can be a virtual post-it or three-dimensional virtual model of object overlaid onto the real-world view. Experimental results show that the proposed system works well in real-time with high performance and it can be used as a mobile service for personalized messaging.

• Journal of Korea Spatial Information System Society
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• v.7 no.2 s.14
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• pp.67-79
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• 2005

For tracing tag locations, the trajectories should be modeled and indexed in a radio frequency identification (RFID) system. The trajectory of a tag is represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that a tag stays in a reader is missing from the trajectory represented only as a point, it is impossible to find the tag that remains in a reader. To solve this problem we propose the data model in which trajectories are defined as intervals and new index scheme called the Interval R-tree. We also propose new insert and split algorithms to enable efficient query processing. We evaluate the performance of the proposed index scheme and compare it with the R-tree and the R*-tree. Our experiments show that the new index scheme outperforms the other two in processing queries of tags on various datasets.