Action categorization based on arm pose modeling

This paper proposes a novel method to categorize human action based on arm pose modeling. Traditionally, human action categorization relies much on the extracted features from video or images. In this research, we exploit the relationship between action categorization and arm pose modeling, which can be visualized in a probabilistic graphical model. Given visual observations, they can be estimated by maximum a posteriori (MAP) in that arm poses are first estimated under the hypothesis of action category by dynamic programming, and then action category hypothesis is validated by soft-max model based on the estimated arm poses. The prior distribution of each action is estimated by a semi-parametric estimator in advance, and pixel-based dense features including LBP, SIFT, colour-SIFT, and texton are utilized to enhance the likelihood computation by the Joint Adaboosting algorithm. The proposed method has been evaluated on images of walking, waving and jogging from the HumanEva-I dataset. It is found to have arm pose modeling performance better than the method of mixtures of parts, and action categorization success rate of 96.69%.