International Symposium on Visual Computing

Advances in Visual Computing pp 647-656 | Cite as

Analyzing Activities in Videos Using Latent Dirichlet Allocation and Granger Causality

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)

Abstract

We propose an unsupervised method for analyzing motion activities from videos. Our method combines Latent Dirichlet Allocation with Granger Causality to discover the main motions composing the activity as well as to detect how these motions relate to one another in time and space. We tested our method on synthetic and real-world datasets. Our method compares favorably with state-of-the-art methods.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Vision Laboratory, Department of Computer SciencesFlorida Institute of TechnologyMelbourneUSA

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