NDLI: Trajectory analysis and semantic region modeling using a nonparametric Bayesian model

Content Provider	IEEE Xplore Digital Library
Author	Xiaogang Wang Keng Teck Ma Gee-Wah Ng Grimson, W.E.L.
Copyright Year	2008
Description	Author affiliation: CS & AI Lab., MIT, Cambridge, MA (Xiaogang Wang)
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.
Starting Page	1
Ending Page	8
File Size	7506110
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Radar tracking Layout Surveillance Videos Radar imaging Trajectory Signal analysis Artificial intelligence Laboratories
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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