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Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
Abstract
This paper compares different similarity measures used for trajectory clustering in outdoor surveillance scenes. Six similarity measures are presented and the performance is evaluated by correct clustering rate (CCR) and time cost (TC). The experimental results demonstrate that in outdoor surveillance scenes, the simpler PCA+Euclidean distance is competent for the clustering task even in case of noise, as more complex similarity measures such as DTW, LCSS are not efficient due to their high computational cost
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Cited by
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
Morris, B. T., & Trivedi, M. M. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.64 · Cited by 349
A review of moving object trajectory clustering algorithms
Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-016-9477-7 · Cited by 323
Clustering of Vehicle Trajectories
Atev, S., Miller, G., & Papanikolopoulos, N. P. (2010) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2010.2048101 · Cited by 257
Metadata
- Authors
- Zhang, Z., Huang, K., & Tan, T.
- Venue
- 18th International Conference on Pattern Recognition (ICPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 1135-1138
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2151263703
- Cited by
- 240
- References
- 0
- FWCI
- 9.49
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, Z., Huang, K., & Tan, T. (2006). Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes. 18th International Conference on Pattern Recognition (ICPR'06). https://doi.org/10.1109/icpr.2006.392
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