• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.331-341
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• 2002

In order to extract 3-dimensional information from 2-D image, stereo images are prerequisite. Moreover, for the measurement of moving objects, the synchronized sequential stereo images have to be captured and image matching should be implemented for determining the location of moving objects. In this research, a simple method computing 3-dimensional coordinates from sequential images of moving objects was implemented. The sequential stereo images were captured by a video camera with a beam splitter. Once video images were digitalized by frame grabber, the interest points were extracted and matched in each stereo image, and the coordinates of center of them are calculated using weighted average method. Then, 3-dimensional coordinates of moving objects were computed by DLT algorithms.

• Journal of Platform Technology
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• v.11 no.1
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• pp.52-59
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• 2023

The importance of spatio-temporal trajectories for contact tracing has increased due to the recent COVID-19 pandemic. Spatio-temporal trajectories store time and spatial data of moving objects. In this paper, I propose query processing for spatio-temporal trajectories of moving objects. The spatio-temporal trajectory model of moving objects has point type spatial data for storing locations and timestamp type temporal data for time. A trajectory query is a query to search for pairs of users who have been in close contact by boarding the same bus. To process the trajectory query, I use the Geolife dataset provided by Microsoft. The proposed trajectory query processing method divides trajectory data by date and checks whether users' trajectories were nearby for each date to generate information about contacts as the result.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.979-994
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• 2008

Moving objects are spatio-temporal data that change their location or shape continuously over time. Generally, if continuously moving objects are managed by a conventional database management system, the system cannot properly process the past and future location which is not stored in the database. Up to now, for the purpose of location tracking which is not stored, the linear interpolation to estimate the past location has been usually used. It is suitable for the moving objects on linear route, not curved route. In this paper, we propose a past location tracking algorithm for a moving object on curved routes, and also suggest a future location tracking algorithm using some past location information. We found that the proposed location tracking algorithm has higher accuracy than the linear interpolation function.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.38-45
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• 2001

In this paper, we propose an algorithm for moving object tracking from moving camera using histogram back program intersection(HI) and XY-projection The proposed method segments objects using histogram back projection, matches tracing objects using histogram intersection and extracts them using XY- projection. Through the simulation this paper shows that the proposed method segments. matches and tracks objects without significant error image sequences obtained by moving camera.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.832-837
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• 2002

As diverse researches are working about location acquisition, storing method, data modeling and query processing of moving objects, the moving object database systems, which can gain, store and manage location information and query processing, are tuning up. As the mobile device is moving but have constraints, the convenience user interface for spatio-temporal query and viewing query result needs. In this paper, we designed user Interface for spatio-temporal query related moving objects, viewing query result, tracing current and past location of those and monitoring. And we designed system for implementation of these interfaces.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.177-180
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• 2006

In this paper we proposed a method of automatic point segmentation acquired by 2D laser scanner to recognize moving objects. Recently, Laser scanner is noticed as a new method in the field of close range 3D modeling. But the majority of the researches are pointed on precise 3D modeling of static objects using expensive 3D laser scanner. 2D laser scanner is relatively cheap and can obtain 2D coordinate information of moving object's surface or can be utilized as 3D laser scanner by rotating the system body. In these reasons, some researches are in progress, which are adopting 2D laser scanner to robot control systems or detection of objects moving along linear trajectory. In our study, we automatically segmented point data of 2D laser scanner thus we could recognize each of the object passing through a section.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.71-75
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• 2009

In this paper, we present a real-time method to detect moving objects in a rotating and zooming camera. It is useful for camera surveillance of fixed but rotating camera, camera on moving car, and so on. We first compensate the global motion, and then exploit the displaced frame difference (DFD) to find the block-wise boundary. For robust detection, we propose a kind of image to combine the detections from consecutive frames. We use the block-wise detection to achieve the real-time speed, except the pixel-wise DFD. In addition, a fast block-matching algorithm is proposed to obtain local motions and then global affine motion. In the experimental results, we demonstrate that our proposed algorithm can handle the real-time detection of common object, small object, multiple objects, the objects in low-contrast environment, and the object in zooming camera.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.15 no.2
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• pp.185-193
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• 1997

While close range photogrammetry has been widely applied for static deformation analysis, video cameras have many characteristics that make them the sensors of choice for dynamic analysis of rapidly changing situations. They also have limitations. The aim of this research is to explore the potential of a video system for monitoring dynamic objects. A pilot system consists of two camcorders, VCR, and PC with frame grabber. To estimte the performance of this system for moving objects, a car was imaged covering several phases when starting to drive. The sequential images of a moving car were recorded on VCR. 15 images per second were digitized in an off-line mode by frame grabber. The image coordinates of targets attached to the rear bumper of a car were acquired by IDRISI, and the object coordinates were derived based on DLT. This research suggests that home video cameras, PC, and photogrammetric principles are promising tools for monitoring of the moving objects and vibrations as well as other time dependent situations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.135-143
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• 1996

In this paper, we popose a neural network that detects edges of moving objects in an image using a neural network of hierarchical spatial filter with orientation selectivity. We modify the temporal difference network by adding a self loop to each neuraon cell to reduce the problems of phantom edge detected by the neural network proposed by kwon yool et al.. The modified neural network alleviates the phantom edges of moving objects, and also can detect edges of miving objects even for the noisy input. By computer simulation with real images, the proposed neural netowrk can extract edges of different orientation efficiently and also can reduce the phantom edges of moving objects.