• Journal of KIISE:Databases
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• v.30 no.1
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• pp.14-26
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• 2003

Moving objects' trajectories play an important role in indexing video data on their content and semantics for content-based video retrieval. In this paper, we propose new similar sub-trajectory retrieval schemes based on k-warping algorithm for efficient retrieval on moving objects' trajectories in video data. The proposed schemes are fixed-replication similar sub-trajectory retrieval(FRSR) and variable-replication similar sub-trajectory retrieval(VRSR). The former can replicate motions with a fixed number for all motions being composed of the trajectory. The latter can replicate motions with a variable number. Our schemes support multiple properties including direction, distance, and time interval as well as a single property of direction, which is mainly used for modeling moving objects' trajectories. Finally, we show from our experiment that our schemes outperform Li's scheme(no-warping) and Shan's scheme(infinite-warping) in terns of precision and recall measures.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.124-126
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• 2002

본 논문에서는 비디오 데이터가 지니는 이동 객체의 움직임 궤적(moving objects'trajectories)에 대해 유사 부분 움직임 궤적 검색을 효율적으로 지원하는 N-워핑(N-warping) 알고리즘을 제안한다. 제안하는 알고리즘은 기존의 시계열 데이터베이스에서 유사 서브시퀸스 검색을 위해 사용되었던 타임 워핑 변환 기법(time-warping transformation)을 변형란 알고리즘이다. 또한 제안하는 알고리즘은 움직임 궤적을 모델링하기 위해 사용되는 단일 속성(property)인 각도뿐만 아니라, 거리와 시간과 같은 다중 속성을 지원하며, 사용자 질의에 대해 유사 부분 움직임 궤적 검색을 가능하게 하는 근사 매칭(approximate matching)을 지원한다

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.183-189
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• 2009

Recently, PCS, PDA and mobile devices, such as the proliferation of spread, GPS (Global Positioning System) the use of, the rapid development of wireless network and a regular user even images, audio, video, multimedia data, such as increased use is for. In particular, video data among multimedia data, unlike the moving object, text or image data that contains information about the movements and changes in the space of time, depending on the kinds of changes that have sigongganjeok attributes. Spatial location of objects on the flow of time, changing according to the moving object (Moving Object) of the continuous movement trajectory of the meeting is called, from the user from the database that contains a given query trajectory and data trajectory similar to the finding of similar trajectory Search (Similar Sub-trajectory Retrieval) is called. To search for the trajectory, and these variations, and given the similar trajectory of the user query (Tolerance) in the search for a similar trajectory to approximate data matching (Approximate Matching) should be available. In addition, a large multimedia data from the database that you only want to be able to find a faster time-effective ways to search different from the existing research is required. To this end, in this paper effectively divided into a grid to search for the trajectory to the trajectory of moving objects, similar to the effective support of the search trajectory offers a new grid-based search techniques.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.247-258
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• 2004

Recently, there have been researches on storage and retrieval technique of moving objects, which are highly concerned by user in database application area such as video databases, spatio-temporal databases, and mobile databases. In this paper, we propose a new signature-based indexing scheme which supports similar sub-trajectory retrieval at well as good retrieval performance on moving objects trajectories. Our signature-based indexing scheme is classified into concatenated signature-based indexing scheme for similar sub-trajectory retrieval, entitled CISR scheme and superimposed signature-based indexing scheme for similar sub-trajectory retrieval, entitled SISR scheme according to generation method of trajectory signature based on trajectory data of moving object. Our indexing scheme can improve retrieval performance by reducing a large number of disk access on data file because it first scans all signatures and does filtering before accessing the data file. In addition, we can encourage retrieval efficiency by appling k-warping algorithm to measure the similarity between query trajectory and data trajectory. Final]y, we evaluate the performance on sequential scan method(SeqScan), CISR scheme, and SISR scheme in terms of data insertion time, retrieval time, and storage overhead. We show from our experimental results that both CISR scheme and SISR scheme are better than sequential scan in terms of retrieval performance and SISR scheme is especially superior to the CISR scheme.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.437-440
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• 2006

본 논문에서는 도로 네트워크에서 이동하는 객체들의 미래 경로를 예측하는 방안에 대하여 다룬다. 기존의 대부분 미래 예측 기법들을 유클리드 공간에서 이동하는 객체들을 대상으로 한다. 그러나 텔레매틱스 등 다양한 응용에서 객체들은 도로 네트워크 상에서 이동하는 경우가 많으므로 이를 위한 미래 예측 방법이 요구된다. 본 연구에서는 질의 객체의 현재까지의 이동 궤적과 유사한 경향을 가지는 과거 궤적들을 분석함으로써 이 객체의 미래 경로를 예측하는 기법을 제안한다. 우선, 도로 네트워크의 특성을 미래 경로를 다음과 같이 예측한다. 먼저, 이동 객체 데이터베이스 내의 과거 궤적들을 대상으로 주어진 질의 궤적과 유사한 부분 궤적을 갖는 후보 궤적들을 검색한다. 그 다음, 검색된 후보 궤적들의 현재 위치 이후부터 목적지까지의 이동 경로를 분석함으로써 객체의 미래 이동 경로를 예측한다. 작은 차이를 갖는 이동 경로들을 같은 그룹으로 간주함으로써 경로 예측의 정확성을 높이는 방안을 제안한다.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.