{"title":"Traj Align: A Method for Precise Matching of 3-D Trajectories","authors":"Z. Aung, Kelvin Sim, W. Ng","doi":"10.1109/ICPR.2010.930","DOIUrl":null,"url":null,"abstract":"Matching two 3-D trajectories is an important task in a number of applications. The trajectory matching problem can be solved by aligning the two trajectories and taking the alignment score as their similarity measurement. In this paper, we propose a new method called \"TrajAlign\" (Trajectory Alignment). It aligns two trajectories by means of aligning their representative distance matrices. Experimental results show that our method is significantly more precise than the existing state-of-the-art methods. While the existing methods can provide correct answers in only up to 67% of the test cases, TrajAlign can offer correct results in 79% (i.e. 12% more) of the test cases, TrajAlign is also computationally inexpensive, and can be used practically for applications that demand efficiency.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 20th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2010.930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Matching two 3-D trajectories is an important task in a number of applications. The trajectory matching problem can be solved by aligning the two trajectories and taking the alignment score as their similarity measurement. In this paper, we propose a new method called "TrajAlign" (Trajectory Alignment). It aligns two trajectories by means of aligning their representative distance matrices. Experimental results show that our method is significantly more precise than the existing state-of-the-art methods. While the existing methods can provide correct answers in only up to 67% of the test cases, TrajAlign can offer correct results in 79% (i.e. 12% more) of the test cases, TrajAlign is also computationally inexpensive, and can be used practically for applications that demand efficiency.