Segmentation and classification of modeled actions in the context of unmodeled ones
In Proceedings British Machine Vision Conference 2014
AbstractIn this work, we provide a discriminative framework for online simultaneous segmentation and classification of visual actions, which deals effectively with unknown sequences that may interrupt the known sequential patterns. To this end we employ Hough transform to vote in a 3D space for the begin point, the end point and the label of the segmented part of the input stream. An SVM is used to model each class and to suggest putative labeled segments on the timeline. To identify the most plausible segments among the putative ones we apply a dynamic programming algorithm, which maximizes an objective function for label assignment in linear time. The performance of our method is evaluated on synthetic as well as on real data (Berkeley multimodal human action database and Weizmann). The proposed approach is of comparable accuracy to the state of the art for online stream segmentation and classification and performs considerably better in the presence of previously unseen activities.
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