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Action Recognition

Action Recognition

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Action Recognition

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  1. Action Recognition Karthik Prabhakar UCF REU 2008, Week 2 Report May 30, 2008

  2. Research Topic • Action Recognition in videos • (ala Mikel’s presentation on Wednesday) • Goals • Perform under unconstrained environments • Achieve (semi) time-invariance

  3. Summary of Niebleset al. • “Unsupervised Learning of Human Action Categories using Spatio-Temporal Words” • Video sequences represented as collection of spatio-temporal ‘words’. • Using pLSA and LDA, the system learns the probability distributions of the spatial-temporal words and the topics corresponding to human action categories.

  4. LDA (Latent Dirichlet Allocation) • A generative-model that allows a set of observations to be explained by unobserved groups (these explain why some parts of the data are similar). Priors Video sequences Action categories Spatio-temporal words

  5. Proposed (Initial) Model

  6. Goals/Tasks for next week • Finalize a set of descriptors/features • Implement/Get DTM • Implement the system • (hopefully) Get some test videos running!