Exploring movement-similarity analysis of moving objects

Dodge, Somayeh; Weibel, Robert; Laube, Patrick

doi:10.1145/1645424.1645427

Cited by 18 publications

(13 citation statements)

References 29 publications

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“…Several measures exist for calculating the similarity between two trajectories, each with their own strengths and weaknesses. Several surveys of trajectory similarity measures have been performed [7,11,18]. After first outlining some of the useful applications of trajectory similarity measures, four of the most commonly used similarity measures will be discussed in detail: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance.…”

Section: Introductionmentioning

confidence: 99%

Trajectory similarity measures

2015

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Storing, querying, and analyzing trajectories is becoming increasingly important, as the availability and volumes of trajectory data increases. One important class of trajectory analysis is computing trajectory similarity. This paper introduces and compares four of the most common measures of trajectory similarity: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance. These four measures have been implemented in a new open source R package, freely available on CRAN [19]. The paper highlights some of the differences between these four similarity measures, using real trajectory data, in addition to indicating some of the important emerging applications for measurement of trajectory similarity.

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Section: Introductionmentioning

confidence: 99%

Trajectory similarity measures

2015

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“…Understanding the movement itself, as well as the patterns of movement is very important in many areas of science and technology (Dodge 2011). Capturing trajectory data at fine temporal and spatial granularities has allowed representation, and consequently analysis, of detailed geospatial lifelines.…”

Section: Analysis Of Movement Patternmentioning

confidence: 99%

Statistical Analysis of Sport Movement Observations: The Case of Orienteering

Amouzandeh

Karimipour

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Study of movement observations is becoming more popular in several applications. Particularly, analyzing sport movement time series has been considered as a demanding area. However, most of the attempts made on analyzing movement sport data have focused on spatial aspects of movement to extract some movement characteristics, such as spatial patterns and similarities. This paper proposes statistical analysis of sport movement observations, which refers to analyzing changes in the spatial movement attributes (e.g. distance, altitude and slope) and non-spatial movement attributes (e.g. speed and heart rate) of athletes. As the case study, an example dataset of movement observations acquired during the "orienteering" sport is presented and statistically analyzed.

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“…Among them, some contributed to the clustering of moving objects (e.g., [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41]), the mining of movement patterns (e.g., [34,38,[43][44][45]) and the exploration of similarity of moving objects (e.g., [41,[46][47][48][49]). The idea behind the approach proposed in this paper is to contribute to all three classes of knowledge discovery, i.e., mining patterns, similarity assessment, and clustering.…”

Section: Clustering Of Movementsmentioning

confidence: 99%

Movement Pattern Analysis Based on Sequence Signatures

Chavoshi

Baets

Neutens

et al. 2015

IJGI

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Increased affordability and deployment of advanced tracking technologies have led researchers from various domains to analyze the resulting spatio-temporal movement data sets for the purpose of knowledge discovery. Two different approaches can be considered in the analysis of moving objects: quantitative analysis and qualitative analysis. This research focuses on the latter and uses the qualitative trajectory calculus (QTC), a type of calculus that represents qualitative data on moving point objects (MPOs), and establishes a framework to analyze the relative movement of multiple MPOs. A visualization technique called sequence signature (SESI) is used, which enables to map QTC patterns in a 2D indexed rasterized space in order to evaluate the similarity of relative movement patterns of multiple MPOs. The applicability of the proposed methodology is illustrated by means of two practical examples of interacting MPOs: cars on a highway and body parts of a samba dancer. The results show that the proposed method can be effectively used to analyze interactions of multiple MPOs in different domains.

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Exploring movement-similarity analysis of moving objects

Cited by 18 publications

References 29 publications

Trajectory similarity measures

Trajectory similarity measures

Statistical Analysis of Sport Movement Observations: The Case of Orienteering

Movement Pattern Analysis Based on Sequence Signatures

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