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To address recognition of human actions under view changes, this paper proposes a view-invariant human action recognition approach based on local linear dynamical system and sparse coding. We utilize the bag-of-words (BoW) approach, local patches are modeled as linear dynamical systems and the model parameters are used as the descriptors of local patches. The model parameters capture the dynamics...
Much of action recognition research is recently based on a bag of words (BOW) representation by quantizing the extracted 3D interest points from videos. The k-means algorithm is commonly used to construct a visual vocabulary. However, it has two major drawbacks. Firstly, the visual vocabulary is sensitive to the vocabulary size and the initialization. Secondly, k-means is unable to capture the salient...
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