A Framework for Online Segmentation and Classification of Modeled Actions Performed in the Context of Unmodeled Ones

In this paper, we propose a discriminative framework for online simultaneous segmentation and classification of modeled visual actions that can be performed in the context of other unknown actions. 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. A support vector machine 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 the likelihood for label assignment in linear time. The performance of our method is evaluated on synthetic as well as on real data (Weizmann, TUM Kitchen, UTKAD, and Berkeley Multimodal Human Action databases). Extensive quantitative results obtained on a number of standard data sets demonstrate that the proposed approach is of comparable accuracy with the state-of-the-art approaches for online stream segmentation and classification when all performed actions are known, and performs considerably better in the presence of unmodeled actions.