To adapt to occlusion or other complex situations

an action recognition method is proposed which fuses multiple pose estimation features. Multiple pose features will be obtained using multiple action models. Each pose feature information includes key point positions and pose scores. Distinguishing key pointsare extracted from all train images and computing relative distances between point pairs. An action template is built using all features of the train images of the action. Multiple feature information consistent with multiple action templates are extracted from each test image from multiple pose features.Multiple features of the test image are-matched with the corresponding action template and then matched values are optimized using pose scores. The experimental results have shown that the average accuracy of the proposed method is approximately 2% better than some other state-of-the-art methods on VOC 2011-val set

and is approximately 6% better than some other state-of-the-art methods on Stanford 40 actions set. By fusing multiple pose features

the proposed method can adapt to occlusion and other complex situations and improve average recognition accuracy.