Abstract
We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition. Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately. Then, all view-dependent manifolds are automatically combined to discover a unified representation which model in a single three dimensional space an action independently from style and viewpoint. In addition, a bidirectional nonlinear mapping function is incorporated to allow projecting actions between original and embedded spaces. The proposed framework is evaluated on a real and challenging dataset (IXMAS), which is composed of a variety of actions seen from arbitrary viewpoints. Experimental results demonstrate robustness against style and view variation and match the most accurate action recognition method.
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Keywords
- Action Recognition
- Human Action Recognition
- Action Descriptor
- Visual Hull
- Nonlinear Dimensionality Reduction
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Lewandowski, M., Makris, D., Nebel, JC. (2010). View and Style-Independent Action Manifolds for Human Activity Recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15567-3_40
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DOI: https://doi.org/10.1007/978-3-642-15567-3_40
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