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Human action recognition with line and flow histograms

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2008-12-11
İKİZLER CİNBİŞ, NAZLI
Cinbiş, Ramazan Gökberk
DUYGULU ŞAHİN, PINAR
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.