CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.05 - May

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Issue No.05 - May (2013 vol.35)

pp: 1051-1065

Weiming Hu , Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China

Xi Li , Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China

Guodong Tian , Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China

S. Maybank , Dept. of Comput. Sci. & Inf. Syst, Birkbeck Coll., London, UK

Zhongfei Zhang , Dept. of Comput. Sci., Binghamton Univ., Binghamton, NY, USA

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.

INDEX TERMS

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

CITATION

Weiming Hu, Xi Li, Guodong Tian, S. Maybank, Zhongfei Zhang, "An Incremental DPMM-Based Method for Trajectory Clustering, Modeling, and Retrieval",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.35, no. 5, pp. 1051-1065, May 2013, doi:10.1109/TPAMI.2012.188REFERENCES