Optimized Discrete Frechet Distance between trajectories

被引:14
作者
Devogele, Thomas [1 ]
Etienne, Laurent [1 ]
Esnault, Maxence [1 ]
Lardy, Florian [1 ]
机构
[1] Univ Tours, Comp Sci Lab, 64 Ave Jean Portalis, F-37200 Tours, France
来源
BIGSPATIAL 2017: PROCEEDINGS OF THE 6TH ACM SIGSPATIAL INTERNATIONAL WORKSHOP ON ANALYTICS FOR BIG GEOSPATIAL DATA (BIGSPATIAL-2017) | 2017年
关键词
Frechet distance; linear distance; similarity measure; GPS dataset; filtering process; trajectory mining; RECOMMENDATIONS; LEADERSHIP; TIME;
D O I
10.1145/3150919.3150924
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Distance computation between polylines or trajectories is a key point to assess similarity between geometrical objects. This paper describes a new optimized algorithm to compute discrete Frechet distance which aims to lower computation time and improve precision. This algorithm is applied to GPS trajectories. It includes a filtering, pruning and an enhancement process. Thanks to this algorithm, big data trajectory repositories can be mined. This process is validated on a large trajectory dataset.
引用
收藏
页码:11 / 19
页数:9
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