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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Revisiting LBP-Based Texture Models for Human Action Recognition
verfasst von : Thanh Phuong Nguyen, Antoine Manzanera, Ngoc-Son Vu, Matthieu Garrigues
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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A new method for action recognition is proposed by revisiting LBP-based dynamic texture operators. It captures the similarity of motion around keypoints tracked by a realtime semi-dense point tracking method. The use of self-similarity operator allows to highlight the geometric shape of rigid parts of foreground object in a video sequence. Inheriting from the efficient representation of LBP-based methods and the appearance invariance of patch matching method, the method is well designed for capturing action primitives in unconstrained videos. Action recognition experiments, made on several academic action datasets validate the interest of our approach.