A novel action recognition framework based on integrated model is proposed in the paper. First, the covariance descriptor is utilized to extract features from video sequences, and then each class specific codebook is constructed and appended to the global codebook. A static model applying the template matching technique and a dynamic model employing the trigram model are learned to capture complementary information in an action. And lastly, an integrated model is used to estimate the confidence of the static and dynamic models and produces a reliable result. Comparative experiments show that our presented method achieves superior results over other state-of-the-art approaches.
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