Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy-Reference-Cited by-同舟云学术

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Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

Published:2017-09-12 Issue:5 Volume:22 Page:1433-1444
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Song Huansheng,Wang Xuan,Hua Cui,Wang Weixing,Guan Qi,Zhang Zhaoyang

Funder

Natural Science Foundation of China (CN)

Natural Science Foundation of Shaanxi Province

Fundamental Research Funds for the Central Universities of China

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/article/10.1007/s00500-017-2831-0/fulltext.html

Reference26 articles.

1. Abraham S, Lal PS (2012) Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations. Transp Res Part C 23:109–123

2. Alcantarilla PF, Bartoli A, Davison AJ (2012) Kaze features. In: Proceedings of European conference on computer vision

3. Atev S, Miller G, Papanikolopoulos N (2010) Clustering of vehicle trajectories. IEEE Trans Intell Transp Syst 11(3):647–657

4. Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: Proceedings of European conference on computer vision

5. Beymer D, McLauchlan P, Coifman B, Malik J (1997) A real-time computer vision system for measuring traffic parameters. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 495–501

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