Abstract
Human action recognition is to judge human action by analyzing action characteristics in the fields of computer vision and video surveillance. As the development of machine learning technique, the application of Bayesian Learning Model is increasing in related fields. In order to analyze the characteristics of human action and then recognize human action, this paper introduce a survey on Bayesian Learning Model for Human Action Recognition. The paper focuses on Bayesian handcrafted and deep learning models, and evaluate the state-of-the-art benchmark datasets, e.g., Weizmann, KTH, MSR-3D, HOHA, and UCF101. In this paper, all papers are published ranging from 2007 to 2016, which provides an overview of the progress in this area.
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