Hu, W. and Li, X. and Tian, G. and Maybank, Stephen J. and Zhang, Z. (2013) An incremental DPMM-based method for trajectory clustering, modeling, and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (5), pp. 1051-1065. ISSN 0162-8828.
Abstract
Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | trajectory, discrete Fourier transforms, videos, clustering algorithms, vectors, hidden Markov models, feature extraction, video retrieval, trajectory clustering and modeling, incremental clustering, Dirichlet process mixture model, time-sensitive Dirichlet process mixture model |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 25 Jul 2013 10:49 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7776 |
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