V-JAUNE: A framework for joint action recognition and video summarization

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Journal Article
ACM Transactions on Multimedia Computing, Communications and Applications, 2017, 13 (2)
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© 2017 ACM. Video summarization and action recognition are two important areas of multimedia video analysis. While these two areas have been tackled separately to date, in this article, we present a latent structural SVM framework to recognize the action and derive the summary of a video in a joint, simultaneous fashion. Efficient inference is provided by a submodular score function that accounts for the action and summary jointly. In this article, we also define a novel measure to evaluate the quality of a predicted video summary against the annotations of multiple annotators. Quantitative and qualitative results over two challenging action datasets-the ACE and MSR DailyActivity3D datasets-show that the proposed joint approach leads to higher action recognition accuracy and equivalent or better summary quality than comparable approaches that perform these tasks separately.
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