Dictionary of gray-level 3D patches for action recognition

This paper deals with action recognition in the context of video analysis based on the use of a sparse dictionary defined by the 3D spatio-temporal representation of the actions. A 3D volume can be seen as a set of gray-level 3D patches comprising 2D patches taken in successive frames in order to ca...

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Bibliographic Details
Published in2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) pp. 1 - 6
Main Authors Tim, Stefen Chan Wai, Rombaut, Michele, Pellerin, Denis
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2014
Subjects
Accuracy
Action recognition
Context
Dictionaries
Dictionary learning
Histograms
K-SVD
Spatio-temporal patches
Support vector machines
Three-dimensional displays
Videos
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Summary:This paper deals with action recognition in the context of video analysis based on the use of a sparse dictionary defined by the 3D spatio-temporal representation of the actions. A 3D volume can be seen as a set of gray-level 3D patches comprising 2D patches taken in successive frames in order to capture a motion pattern. The goal of our proposal is to recognize human actions within these 3D volumes whose 3D patches are described with the dictionary atoms. To that end, we compute a motion signature by building a histogram based on the use of the atoms of the dictionary. Paired with a SVM, we show that these signatures can be exploited in the context of action recognition. This method has been tested on the KTH database with good results.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2014.6958858