Abstract
Automatic classification of human actions in video streams is considered one of the promising fields in computer vision, especially in the fields of video indexing, gesture recognition, and surveillance of sensitive assets. In this paper, we presented a sequential based approach for action recognition based on random sample consensus (RANSAC) matching of shape-based influential keypoints. The scale-invariant feature transform (SIFT) is used to provide an invariant descriptor for the action in the entire frame. Then a hypothesize matching is performed between the keypoints in reference and input frames using Normalized Cross Correlation (NCC). The initial matching from NCC is improved using RANSAC, which is used to find a consistent matching and to build the homography. Finally, Hausdorff distance is used for action recognition by measuring the closeness of the two sets. Experimental results show the effectiveness and accuracy of the proposed approach.
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