Human action recognition with line and flow histograms

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Date

2008-12-11

Author

İKİZLER CİNBİŞ, NAZLI
Cinbiş, Ramazan Gökberk
DUYGULU ŞAHİN, PINAR

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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.

Subject Keywords

Humans , Optical computing, Optical filters , Computer vision , Yarn, Optical recording, Image motion analysis , Hidden Markov models, Shape, Histograms

URI

https://hdl.handle.net/11511/37400

DOI

https://doi.org/10.1109/icpr.2008.4761434

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Department of Computer Engineering, Conference / Seminar

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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: https://hdl.handle.net/11511/37400.