• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.217-219
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• 2001

이동 기기의 발달로 이동 객체의 위치 정보에 대한 연구가 진행되고 있다. 그러나 시공간 데이타베이스에서 요구하는 질의를 효과적으로 처리하기 위한 인덱스 구조는 많지 않다. 사용자는 먼저 범위 질의를 던져 이동 객체를 뽑아낸 후 궤적 질의를 던지게 되는데 이것을 결합 질의라 한다. 기존 방법인 TB-tree는 MBR의 겹침이 심해 성능이 저하된다. 본 논문에서는 결합 질의를 효과적으로 처리할 수 있는 CR-tree 인덱스 구조를 제안한다. CR-tree는 R-tree와 연곁노드로 구성된다. R-tree 부분은 MBR의 겹침을 최소화해 범위 질의를 효과적으로 처리하고 연결 노드 부분은 이동 객체별로 궤적의 부분을 차례로 저장해서 궤적 질의를 빠르게 저리한다. 즉 결합 질의시 R-tree를 이용해 범위에 속하는 이동 객체를 검색하고 연결 노드를 이용해 그 객체의 궤적을 찾는다.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1128-1141
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• 2010

This paper proposes a method of moving object detection in crowded scene using clustered trajectory. Unlike previous appearance based approaches, the proposed method employes motion information only to isolate moving objects. In the proposed method, feature points are extracted from input frames first and then feature tracking is followed to create feature trajectories. Based on an assumption that feature points originated from the same objects shows similar motion as the object moves, the proposed method detects moving objects by clustering trajectories of similar motions. For this purpose an energy function based on spatial proximity, motion coherence, and temporal continuity is defined to measure the similarity between two trajectories and the clustering is achieved by minimizing the energy function in CRFs (conditional random fields). Compared to previous methods, which are unable to separate falsely merged trajectories during the clustering process, the proposed method is able to rearrange the falsely merged trajectories during iteration because the clustering is solved my energy minimization in CRFs. Experiment results with three different crowded scenes show about 94% detection rate with 7% false alarm rate.

• The KIPS Transactions:PartD
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• v.12D no.4 s.100
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• pp.507-520
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• 2005

The R-trees are usually used for an index of trajectories in moving-objects databases. However, they need to access a number of nodes to trace same trajectories because of considering only a spatial proximity. Overlaps and dead spaces should be minimized to enhance the performance of range queries in moving-objects indexes. Trajectories of moving-objects should be preserved to enhance the performance of the trajectory queries. In this paper, we propose the TP3DR-tree(Trajectory Preserved 3DR-tree) using clusters of trajectories for range and trajectory queries. The TP3DR-tree uses two split policies: one is a spatial splitting that splits the same trajectory by clustering and the other is a time splitting that increases space utilization. In addition, we use connecting information in non-leaf nodes to enhance the performance of combined-queries. Our experiments show that the new index outperforms the others in processing queries on various datasets.

• The KIPS Transactions:PartD
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• v.16D no.2
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• pp.161-168
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• 2009

Recently, use of various position base servicesthat collect position information for moving object and utilize in real life is increasing by the development of wireless network technology. Accordingly, new index structures are required to efficiently retrieve the consecutive positions of moving objects. This paper addresses an improved trajectory split algorithm for the purpose of efficiently supporting spatio-temporal range queries using index structures that use Minimum Bounding Rectangles(MBR) as trajectory approximations. We consider volume of Extended Minimum Bounding Rectangles (EMBR) to be determined by average size of range queries. Also, Use a priority queue to speed up our process. This algorithm gives in general sub-optimal solutions with respect to search space. Our improved trajectory split algorithm is going to derive minimizing volume of EMBRs better than previously proposed split algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.175-178
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• 2015

최근 모바일 장비 및 시스템의 발달로 위치정보와 이동 객체에 관련된 많은 서비스들이 연구되고 있다. 궤적은 이동 객체가 시간에 따라 변하는 위치정보들의 모음이다. 기존의 데이터베이스 시스템에서는 이동 객체 데이터 타입을 지원하지 않는다. 이 논문에서는 공간 데이터베이스로 많이 활용되고 있는 PostgreSQL/PostGIS 상에서 궤적 데이터의 연산 및 질의 처리가 가능한 확장형 모듈로써 PostTrajectory를 제안하고 있다. 또한, 궤적 데이터에 대한 다양한 질의 요구 조건들을 충족시킬 수 있는 함수들을 구현하여 궤적 데이터의 효율적인 질의 처리를 가능하게 하였다.

• 한국공간정보시스템학회:학술대회논문집
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• 2003.11a
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• pp.169-174
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• 2003

이동 객체에 대한 기존 최근접(nearest neighbor, NN) 질의 처리 기법들은 질의 궤적에 대해 연속적으로 정확하게, 질의와 가장 가까운 위치를 유지하면서 움직이는 최근접 객체를 선택할 수 있는 충분한 기준을 가지고 있지 못하다. 이 논문은 질의 객체와 데이터 객체가 모두 이동 객체인 경우에 가장 적합하게 사용되는 객체 궤적에 대한 연속적인 질의 처리를 통해 정확한 결과를 얻을 수 있는 새로운 최근접 질의 처리 기법, 연속 궤적 최근접 질의(CTNN, continuous trajectory nearest neighbor query)를 제안한다. 우리는 두 가지 Approximate, Exact CTNN 기법을 제안하며 이들은 모두 항해 시스템, 교통 통제 시스템, 물류정보 시스템 등 각종 위치 기반 서비스(L8S: location based services) 상에서 다양하게 사용될 수 있다. 이들은 이동 객체 궤적이 미리 알려져 있는 경우 그리고 질의와 데이터 객체가 모두 이동 객체인 경우에 가장 적합하다.

• Spatial Information Research
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• v.19 no.2
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• pp.57-64
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• 2011

This paper describes how to storing and querying trajectory information on PostgreSQL/PostGIS. Recently as technology of mobile devices is advancing, many researches for location-based services and moving object's trajectory have been studied. Trajectory is the set of information of the location by the time, and is one of the most im portant information for location-based services. Traditional spatial database systems do not support trajectory data types and functions. In this paper, we propose a trajectory data type and query functions for moving objects on PostgreSQL/PostGIS.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.75-78
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• 2005

텔레매틱스 서비스를 위한 많은 어플리케이션들이 개발 됨에 인해 테스트를 위한 도로 네트워크 기반의 이동객체 궤적데이터의 필요성이 증대되고 있다. 본 논문에서는 도로 네트워크 상의 이동객체들의 실 궤적 데이터와 유사한 합성 궤적 데이터를 구축하기 위한 방법론을 제안한다. 그리고 실제 구현 결과와 실 데이터와의 속도 패턴을 비교하여 실 데이터와의 유사성을 보인다.

• 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.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.1-18
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• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.