Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 1.62 MB
Add to list
  1. Search results
  2. Movement pattern analysis based on se...

Movement pattern analysis based on sequence signatures

Seyed Hossein Chavoshi (UGent) , Bernard De Baets (UGent) , Tijs Neutens (UGent) , Matthias Delafontaine (UGent) , Guy De Tré (UGent) and Nico Van de Weghe (UGent)
(2015) ISPRS International Journal of Geo-Information. 4(3). p.1605-1626
Author
Organization
Abstract
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.
Keywords
SYSTEMS, BLUETOOTH, SPACE, TRAJECTORIES, INFORMATION, moving point objects (MPO), movement patterns, qualitative trajectory calculus (QTC), sequence signature (SESI), similarity analysis, REPRESENTING MOVING-OBJECTS, TIME, KNOWLEDGE DISCOVERY, TRACKING, TOOL

Downloads

  • Download
    Chavoshi movement.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 1.62 MB

Citation

Please use this url to cite or link to this publication:

MLA
Chavoshi, Seyed Hossein, et al. “Movement Pattern Analysis Based on Sequence Signatures.” ISPRS International Journal of Geo-Information, vol. 4, no. 3, 2015, pp. 1605–26, doi:10.3390/ijgi4031605.
APA
Chavoshi, S. H., De Baets, B., Neutens, T., Delafontaine, M., De Tré, G., & Van de Weghe, N. (2015). Movement pattern analysis based on sequence signatures. ISPRS International Journal of Geo-Information, 4(3), 1605–1626. https://doi.org/10.3390/ijgi4031605
Chicago author-date
Chavoshi, Seyed Hossein, Bernard De Baets, Tijs Neutens, Matthias Delafontaine, Guy De Tré, and Nico Van de Weghe. 2015. “Movement Pattern Analysis Based on Sequence Signatures.” ISPRS International Journal of Geo-Information 4 (3): 1605–26. https://doi.org/10.3390/ijgi4031605.
Chicago author-date (all authors)
Chavoshi, Seyed Hossein, Bernard De Baets, Tijs Neutens, Matthias Delafontaine, Guy De Tré, and Nico Van de Weghe. 2015. “Movement Pattern Analysis Based on Sequence Signatures.” ISPRS International Journal of Geo-Information 4 (3): 1605–1626. doi:10.3390/ijgi4031605.
Vancouver
1.
Chavoshi SH, De Baets B, Neutens T, Delafontaine M, De Tré G, Van de Weghe N. Movement pattern analysis based on sequence signatures. ISPRS International Journal of Geo-Information. 2015;4(3):1605–26.
IEEE
[1]
S. H. Chavoshi, B. De Baets, T. Neutens, M. Delafontaine, G. De Tré, and N. Van de Weghe, “Movement pattern analysis based on sequence signatures,” ISPRS International Journal of Geo-Information, vol. 4, no. 3, pp. 1605–1626, 2015.
@article{7221157,
  abstract     = {{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.}},
  author       = {{Chavoshi, Seyed Hossein and De Baets, Bernard and Neutens, Tijs and Delafontaine, Matthias and De Tré, Guy and Van de Weghe, Nico}},
  issn         = {{2220-9964}},
  journal      = {{ISPRS International Journal of Geo-Information}},
  keywords     = {{SYSTEMS,BLUETOOTH,SPACE,TRAJECTORIES,INFORMATION,moving point objects (MPO),movement patterns,qualitative trajectory calculus (QTC),sequence signature (SESI),similarity analysis,REPRESENTING MOVING-OBJECTS,TIME,KNOWLEDGE DISCOVERY,TRACKING,TOOL}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{1605--1626}},
  title        = {{Movement pattern analysis based on sequence signatures}},
  url          = {{http://doi.org/10.3390/ijgi4031605}},
  volume       = {{4}},
  year         = {{2015}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: