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2007
IEEE

Unsupervised Activity Perception by Hierarchical Bayesian Models

10 years 8 days ago
Unsupervised Activity Perception by Hierarchical Bayesian Models
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian model to connect three elements: low-level visual features, simple "atomic" activities, and multi-agent interactions. Atomic activities are modeled as distributions over low-level visual features, and interactions are modeled as distributions over atomic activities. Our models improve existing language models such as Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) by modeling interactions without supervision. Our data sets are challenging video sequences from crowded traffic scenes with many kinds of activities co-occurring. Our approach provides a summary of typical atomic activities and interactions in the scene. Unusual activities and interactions are found, with natural probabilistic explanations. Our method supports flexible high-level queries on activities and interacti...
Xiaogang Wang, Xiaoxu Ma, Eric Grimson
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Xiaogang Wang, Xiaoxu Ma, Eric Grimson
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