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Human action recognition typically requires a large amount of training samples, which is often expensive and time-consuming to create. In this paper, we present a novel approach for enhancing human actions with a limited number of samples via structural average curves analysis. Our approach first learns average sequences from each pair of video samples for every action class and then gather them with...
With the motivation of lower recognition performance as the resolution of processed action videos decreases, this paper presents a robust action recognition approach based on Dempster-Shafer (DS) theory with assumption that single video frames are independent for action discrimination. By the use of artificial neural network (ANN) estimators trained using single video frames, we first compute the...
Sequence matching/alignment based scheme has been common for action recognition. Such a typical scheme, however, requires tremendous amounts of computation when the volume of prototypical action videos is large and easily causes mismatching for border-isolated samples in action categories. In this paper, we propose a framework of averaging video sequences based on multi-dimensional dynamic time warping...
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