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- Publisher Website: 10.1109/CyberC.2012.95
- Scopus: eid_2-s2.0-84872357276
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Conference Paper: A coarse-to-fine approach for motion pattern discovery
| Title | A coarse-to-fine approach for motion pattern discovery |
|---|---|
| Authors | |
| Keywords | GMM motion pattern discovery trajectory data clustering |
| Issue Date | 2012 |
| Citation | Proceedings of the 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2012, 2012, p. 519-522 How to Cite? |
| Abstract | In this paper, we propose a coarse-to-fine approach to discovery motion patterns. There are two phases in the proposed approach. In the first phase, the proposed median-based GMM achieves coarse clustering. Moreover, the number of clusters can be heuristically found by the proposed algorithm. In the second phase, to refine coarse clustering in the first phase, a Fisher optimal division method is proposed to examine the boundary data points and to detect the change point between motion patterns. The experimental results show that the proposed approach outperforms the existing algorithms. © 2012 IEEE. |
| Persistent Identifier | http://hdl.handle.net/10722/336655 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Cai, Bolun | - |
| dc.contributor.author | Luo, Zhifeng | - |
| dc.contributor.author | Li, Kerui | - |
| dc.date.accessioned | 2024-02-29T06:55:36Z | - |
| dc.date.available | 2024-02-29T06:55:36Z | - |
| dc.date.issued | 2012 | - |
| dc.identifier.citation | Proceedings of the 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2012, 2012, p. 519-522 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/336655 | - |
| dc.description.abstract | In this paper, we propose a coarse-to-fine approach to discovery motion patterns. There are two phases in the proposed approach. In the first phase, the proposed median-based GMM achieves coarse clustering. Moreover, the number of clusters can be heuristically found by the proposed algorithm. In the second phase, to refine coarse clustering in the first phase, a Fisher optimal division method is proposed to examine the boundary data points and to detect the change point between motion patterns. The experimental results show that the proposed approach outperforms the existing algorithms. © 2012 IEEE. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Proceedings of the 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2012 | - |
| dc.subject | GMM | - |
| dc.subject | motion pattern discovery | - |
| dc.subject | trajectory data clustering | - |
| dc.title | A coarse-to-fine approach for motion pattern discovery | - |
| dc.type | Conference_Paper | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1109/CyberC.2012.95 | - |
| dc.identifier.scopus | eid_2-s2.0-84872357276 | - |
| dc.identifier.spage | 519 | - |
| dc.identifier.epage | 522 | - |