Abstract
Recognising human activity from video stream has become one of the most interesting applications in computer vision. In this study, a novel hybrid technique for human action recognition is proposed based on fast HOG3D of integral videos and Smith-Waterman partial shape matching of the fused frame. The proposed technique is divided into two main stages, the first stage extracts a set of foreground snippets from the input video, and extracts the histogram of 3D gradient orientations from the spatio-temporal volumetric data; and the second stage fuses a set of key frames from current snippet and extracts the contours from the fused frame. Non-linear support vector machine (SVM) decision trees are used to classify HOG3D features into one of fixed action categories. On the other hand, Smith-Waterman partial shape matching is used to compare between the contour of the fused frame and the stored template contour of specified action. The results from SVM and Smith-Waterman partial shape matching are then combined. The experimental results show that combining non-linear SVM decision trees of HOG3D features and Smith-Waterman partial shape matching of fused contours improved the accuracy of the classification model while maintaining efficiency in time elapsed for training.
Published Version
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