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Trajectory Analysis and Semantic Region Modeling Using A Nonparametric Bayesian Model

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dc.contributor.advisor Eric Grimson en_US Grimson, Eric en_US Wang, Xiaogang en_US Ng, Gee-Wah en_US Ma, Keng Teck en_US
dc.contributor.other Vision en_US 2008-03-17T19:15:17Z 2008-03-17T19:15:17Z 2008-06-24
dc.identifier.other MIT-CSAIL-TR-2008-015 en_US
dc.description.abstract We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are intersections of paths commonly taken by objects, related to activities in the scene are also modeled. Dual-HDP advances the existing Hierarchical Dirichlet Processes (HDP) language model. HDP only clusters co-occurring words from documents into topics and automatically decides the number of topics. Dual-HDP co-clusters both words and documents. It learns both the numbers of word topics and document clusters from data. Under our problem settings, HDP only clusters observations of objects, while Dual-HDP clusters both observations and trajectories. Experiments are evaluated on two data sets, radar tracks collected from a maritime port and visual tracks collected from a parking lot. en_US
dc.format.extent 12 p. en_US
dc.relation Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory en_US
dc.relation en_US
dc.subject hierarchical Dirichlet processes, activity analysis, clustering, visual surveillance en_US
dc.title Trajectory Analysis and Semantic Region Modeling Using A Nonparametric Bayesian Model en_US

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