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Super-resolved Digests of Humans in Video

Abstract : This paper describes a hierarchical approach towards the extraction of highly informative summarized information of humans from video sequences. Objects of interest, such as facial features, are detected through transformation-invariant clustering of the frames, iteratively from bigger to smaller regions, and then expressed with an information-rich representation obtained by super-resolution. To guarantee the fundamental constraints under which the super-resolution process is well-behaved, we propose a Bayesian framework that integrates the uncertainties in the registration of the frames. The ultimate product of the overall process is a strip of images that describe at high resolution the dynamics of the video, switching between alternative local descriptions in response to visual changes.
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https://hal.inria.fr/inria-00325809
Contributor : Peter Sturm <>
Submitted on : Tuesday, September 30, 2008 - 1:53:16 PM
Last modification on : Tuesday, May 25, 2021 - 11:46:03 AM
Long-term archiving on: : Friday, June 4, 2010 - 12:01:16 PM

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Dong Seon Cheng, Marco Cristani, Vittorio Murino. Super-resolved Digests of Humans in Video. The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA'08, Oct 2008, Marseille, France. ⟨inria-00325809⟩

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