• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.65-70
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• 2018

This paper describes a effective information compressor algorithm for the fourth industrial technology. One of the difficult problems for outdoor is to obtain effective updating process of background images. Because input images generally contain the shadows of buildings, trees, moving clouds and other objects, they are changed by lapse of time and variation of illumination. They provide the lowering of performance for surveillance system under outdoor. In this paper, a effective information algorithm for variable environment variable under outdoor is proposed, which apply the Kalman Estimation Modeling and adaptive threshold on pixel level to separate foreground and background images from current input image. In results, the better SNR of about 3dB~5dB and about 10%~25% noise distribution rate in the proposed method. Furthermore, it was showed that the moving objects can be detected on various shadows under outdoor and better result Information.

• ETRI Journal
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• v.29 no.5
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• pp.691-693
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• 2007

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)-based query index. We project a two-dimensional continuous range query on each axis to get two one-dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment-encoded squares-based indexing method, which is one of the most recently proposed methods.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.43-45
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• 2003

Nowadays, with numerous emerging applications (e.g., traffic control, meteorology monitoring, mobile computing, etc.), access methods to process current and future queries for moving objects are becoming increasingly important. Among these methods, the time-parameterized R-tree (TPR-tree) seems likely the most flexible method in one, two, or three-dimensional space. A key point of TPR-tree is that the (conservative) bounding rectangles are expressed by functions of time. In this paper, we propose a new method, which takes into account positions of its moving objects against the rectangle's bounds. In proposed method, the size of bounding rectangle is significantly smaller than the traditional bounding rectangle in many cases. By this approach, we believe that the TPR-tree can improve query performance considerably.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3196-3210
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• 2014

This paper presents a novel video metrology approach based on robust tracking. From videos acquired by an uncalibrated stationary camera, the foreground likelihood map is obtained by using the Codebook background modeling algorithm, and the multiple moving objects are tracked by a combined tracking algorithm. Then, we compute vanishing line of the ground plane and the vertical vanishing point of the scene, and extract the head feature points and the feet feature points in each frame of video sequences. Finally, we apply a single view mensuration algorithm to each of the frames to obtain height measurements and fuse the multi-frame measurements using RANSAC algorithm. Compared with other popular methods, our proposed algorithm does not require calibrating the camera, and can track the multiple moving objects when occlusion occurs. Therefore, it reduces the complexity of calculation and improves the accuracy of measurement simultaneously. The experimental results demonstrate that our method is effective and robust to occlusion.

• Journal of information and communication convergence engineering
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• v.9 no.5
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• pp.545-550
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• 2011

Trilateration is a typical method to locate an object, which requires inherently at least three prerecognized reference points. In some cases, owing to out of reachability to communication facilities the target node cannot be reachable always to three base stations. This paper presents a predictive method, which can identify the location of a moving target node in real time even though the target node could not get in touch with all three base stations. The method is based on the PIDcontrolled Moving Objects Spatio-Temporal Model Algorithm. Simulation results verify that this method can predict the moving direction of a moving target, and then combine with its past position information to judge accurately the location.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.7
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• pp.807-818
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• 1988

This paper proposes new algorithms for the estimation of displacement vector and moving object extraction using difference picture. First, the relations between the boundary of moving objects in two consecutive image and the boundary of difference picture regions are analyzed, then displacement vector estimation algorithm is proposed. Using the estimated displacement vector, moving objects are directly extracted from difference picture. Since the proposed algorithms do not process gray-valued image, they have a short processing time and are suitable to real time processing. From the experimental results, we observed that, if difference picture is wel extracted, the proposecd algorithms work well even in the circumstances of complex background, fast or slow motion, rotation etc., including occlusion where is not moving area.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.171-176
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• 1998

This paper resents a spatio-temporal video segmentation method. The algorithm segments each frame of video sequences captured by a static or moving camera into moving objects (foreground) and background using a statistical hypothesis test. In the proposed method, three consecutive image frames are exploited and a hypothesis testing is performed by comparing two means from two consecutive difference images, which results in a T-test. This hypothesis test yields change detection mask that indicates moving areas (foreground) and non-moving areas (background). Moreover, an effective method for extracting object mask form change detection mask is proposed.

• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1738-1746
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• 2016

This study is on moving images among the various media created by the screen-based image media in the multimedia age. Moving images shown in the image media are reinterpreted in various forms and are necessary to have a conceptual definition as expression media to meet the changes. Therefore, the study identified the conceptual definition of moving images from the perspective of objects, and subdivided them into the visual cognitive area to satisfy the recent characteristics of visual media. It also suggested the guidelines required for analyzing the image media based on the results of the case analysis of 8 different works.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.71-78
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• 2014

The RFID-tag objects can be recognized by a distinct reader where it is installed. The RFID-tag objects are likely described as storages rather than the mobiles in the use of GPS. As RFID tags are large in number compared to moving objects, so the storing and retrieval costs are highly expensive. Here, two solutions for spatio-temporal model taking account of the feature in the tagged objects are proposed. First, the moving-tag objects are expressed by the terms "now" as well as "path location". Second, the size of storing index was noticeably reduced by not saving the tag information of palletizing products but mapping the tagged objects.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.39-52
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• 2006

With the recent the use of spatio-temporal data mining which can extract various knowledge such as movement patterns of moving objects in history data of moving object gets increasing. However, the existing movement pattern extraction methods create lots of candidate movement patterns when the minimum support is low. Therefore, in this paper, we suggest the STMPE(Spatio-Temporal Movement Pattern Extraction) algorithm in order to efficiently extract movement patterns of moving objects from the large capacity of spatio-temporal data. The STMPE algorithm generalizes spatio-temporal and minimizes the use of memory. Because it produces and keeps short-term movement patterns, the frequency of database scan can be minimized. The STMPE algorithm shows more excellent performance than other movement pattern extraction algorithms with time information when the minimum support decreases, the number of moving objects increases, and the number of time division increases.