Human action recognition with line and flow histograms

Cinbiş, Ramazan Gökberk
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.


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Citation Formats
N. İKİZLER CİNBİŞ, R. G. Cinbiş, and P. DUYGULU ŞAHİN, “Human action recognition with line and flow histograms,” 2008, Accessed: 00, 2020. [Online]. Available: