iccv iccv2013 iccv2013-396 iccv2013-396-reference knowledge-graph by maker-knowledge-mining
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Author: Nicolas Ballas, Yi Yang, Zhen-Zhong Lan, Bertrand Delezoide, Françoise Prêteux, Alexander Hauptmann
Abstract: We address the problem of action recognition in unconstrained videos. We propose a novel content driven pooling that leverages space-time context while being robust toward global space-time transformations. Being robust to such transformations is of primary importance in unconstrained videos where the action localizations can drastically shift between frames. Our pooling identifies regions of interest using video structural cues estimated by different saliency functions. To combine the different structural information, we introduce an iterative structure learning algorithm, WSVM (weighted SVM), that determines the optimal saliency layout ofan action model through a sparse regularizer. A new optimization method isproposed to solve the WSVM’ highly non-smooth objective function. We evaluate our approach on standard action datasets (KTH, UCF50 and HMDB). Most noticeably, the accuracy of our algorithm reaches 51.8% on the challenging HMDB dataset which outperforms the state-of-the-art of 7.3% relatively.