송용호
2017-03-27T04:43:05Z
2017-03-27T04:43:05Z
2015-07
Lecture Notes in Electrical Engineering, v. 339, Page. 641-648
978-3-662-46578-3
1876-1100
http://link.springer.com/chapter/10.1007/978-3-662-46578-3_75
http://hdl.handle.net/20.500.11754/26333
Trajectories of moving objects provide fruitful information for analyzing activities of the moving objects; therefore, numerous researches have tried to obtain semantic information from the trajectories by using clustering algorithms. In order to cluster the trajectories, similarity measure of the trajectories should be defined first. Most of existing methods have utilized dynamic programming (DP) based similarity measures to cope with different lengths of trajectories. However, DP based similarity measures do not have enough discriminative power to properly cluster trajectories from the real-world environment. In this paper, an effective trajectory similarity measure is proposed, and the proposed measure is based on the geographic and semantic similarities which have a same scale. Therefore, importance of the geographic and semantic information can be easily controlled by a weighted sum of the two similarities. Through experiments on a challenging real-world dataset, the proposed measure was proved to have a better discriminative power than the existing method.
en
Springer
Video surveillance
Trajectory clustering
Moving objects
Effective trajectory similarity measure for moving objects in real-world scene
Book chapter
339
10.1007/978-3-662-46578-3_75
641-648
Lecture Notes in Electrical Engineering
Ra, Moonsoo
Lim, Chiawei
Song, Yong Ho
Jung, Jechang
Kim, Whoi-Yul
2015031767
S
COLLEGE OF ENGINEERING[S]
DEPARTMENT OF ELECTRONIC ENGINEERING
yhsong