Compressive video classification for decision systems with limited resources

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Authors: G. Tzagkarakis, P. Charalampidis, G. Tsagkatakis, J.-L. Starck, P. Tsakalides
Journal: PCS
Year: 2012
Download: IEEE


In this paper, we address the problem of video classification from a set of compressed features. In particular, the properties of linear random projections in the framework of compressive sensing are exploited to reduce the task of classifying a given video sequence into a problem of sparse reconstruction, based on feature vectors consisting of measurements lying in a low-dimensional compressed domain. This can be of great importance in decision systems with limited power, processing, and bandwidth resources, since the classification is performed without handling the original high-resolution video data, but working directly with the set of compressed measurements. The experimental evaluation verifies the efficiency of the proposed scheme and illustrates that the compressed measurements in conjunction with an appropriate decision rule result in an effective video classification scheme, which meets the constraints of systems with limited resources.

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Author: Samuel Farrens

I have been a postdoctoral researcher at CEA Saclay since October 2015. I am currently working on the DEDALE project and the Euclid mission with Jean-Luc Starck.

My background is in optical detection of clusters of galaxies and photometric redshift estimation. I am now branching out into the field of PSF estimation using sparse signal processing techniques.

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