Human action recognition with line and flow histograms

Date
2008-12
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Source Title
19th International Conference on Pattern Recognition, 2008
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Publisher
IEEE
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Language
English
Type
Conference Paper
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Abstract

We present a compact representation for human action recognition in videos using line and optical flow histograms. We introduce a new shape descriptor based on the distribution of lines which are fitted to boundaries of human figures. By using an entropy-based approach, we apply feature selection to densify our feature representation, thus, minimizing classification time without degrading accuracy. We also use a compact representation of optical flow for motion information. Using line and flow histograms together with global velocity information, we show that high-accuracy action recognition is possible, even in challenging recording conditions. © 2008 IEEE.

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Keywords
Action recognition, Classification time, Compact representation, Feature representation, Feature selection, High-accuracy, Human-action recognition, Motion information, Shape descriptors, Velocity information, Gesture recognition, Graphic methods, Optical flows, Feature extraction
Citation
Published Version (Please cite this version)