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Confidence-based human action recognition with different-level features

Action recognition is a challenging problem in computer vision and is widely studied due to its wide range of applications. Many current approaches have achieved high accuracy on simple actions, but still struggled to recognize interactive behaviors. This work targets on interactive actions between human and objects in some complex real-world situations. Although an action recognition algorithm using high-level ground-truth pose features outperforms the one using low/mid-level global statistical features, pose estimation algorithms which is critical for high-level feature are not reliable enough to interactive actions. In this paper, we propose a novel confidence-based action recognition method. The rationale underlying our method is to make a credible choice of the different-level features between the popular Dense Trajectories based on statistical representations and the high-level pose features based on estimated pose during the classification process according to the predefined confidence. The proposed method is evaluated on the sub-JHMDB dataset. Experimental results suggest that our approach is more accurate than state-of-the-art approaches.