• Title/Summary/Keyword: 프레셰 거리

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Fast Heuristic Algorithm for Similarity of Trajectories Using Discrete Fréchet Distance Measure (이산 프레셰 거리 척도를 이용한 궤적 유사도 고속계산 휴리스틱 알고리즘)

  • Park, Jinkwan;Kim, Taeyong;Park, Bokuk;Cho, Hwan-Gue
    • KIISE Transactions on Computing Practices
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    • v.22 no.4
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    • pp.189-194
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    • 2016
  • A trajectory is the motion path of a moving object. The advances in IT have made it possible to collect an immeasurable amount of various type of trajectory data from a moving object using location detection devices like GPS. The trajectories of moving objects are widely used in many different fields of research, including the geographic information system (GIS) field. In the GIS field, several attempts have been made to automatically generate digital maps of roads by using the vehicle trajectory data. To achieve this goal, the method to cluster the trajectories on the same road is needed. Usually, the $Fr{\acute{e}}chet$ distance measure is used to calculate the distance between a pair of trajectories. However, the $Fr{\acute{e}}chet$ distance measure requires prolonged calculation time for a large amount of trajectories. In this paper, we presented a fast heuristic algorithm to distinguish whether the trajectories are in close distance or not using the discrete $Fr{\acute{e}}chet$ distance measure. This algorithm trades the accuracy of the resulting distance with decreased calculation time. By experiments, we showed that the algorithm could distinguish between the trajectory within 10 meters and the distant trajectory with 95% accuracy and, at worst, 65% of calculation reduction, as compared with the discrete $Fr{\acute{e}}chet$ distance.

Automatic Change Detection Based on Areal Feature Matching in Different Network Data-sets (이종의 도로망 데이터 셋에서 면 객체 매칭 기반 변화탐지)

  • Kim, Jiyoung;Huh, Yong;Yu, Kiyun;Kim, Jung Ok
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.31 no.6_1
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    • pp.483-491
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    • 2013
  • By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.