A novel approach for gesture recognition based on motion history images is proposed in this paper for one-shot learning gesture recognition task. The challenge here is to perform satisfactory recognition operations with only one training example of each action, while no prior knowledge about actions, foreground/background segmentation, or any motion estimation and tracking are available. In the proposed scheme motion history imaging technique is applied to track the motion flow in consecutive frames. The information of motion flow is later utilized to calculate the percent change of motion flow for an action in different spatial regions of the frame. The space-time descriptor computed this way from the query video is a measure of the likeness of a gesture in a lexicon. Finally, gesture classification is performed based on correlation based and Euclidean distance based classifiers and the results are compared. Through extensive experimentations on a much diversified dataset the effectiveness of employing the proposed scheme is established.
This paper deals with the problem of temporal segmentation present in practical applications of action and gesture recognition. In order to separate different gestures from gesture sequences a novel method utilizing depth information, oriented gradients and supervised learning techniques is proposed in this paper. The temporal segmentation task is modeled as a twoclass problem and histogram oriented gradients of the gesture boundary and non-boundary sample frames are incorporated in the feature table as positive and negative training vectors, respectively. The classification task is carried out using both Euclidean Distance based and Support Vector Machine classifiers. A clustering algorithm is employed thereafter to finally locate the temporal boundaries of gestures. Through extensive experimentation it is shown that, the proposed method can provide a high degree of accuracy in temporal gesture segmentation in comparison to a number of recent methods.
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