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Despite recent successes of small object search in images, the search and localization of actions in crowded videos remains a challenging problem because of (1) the large variations of human actions and (2) the intensive computational cost of searching the video space. To address these challenges, we propose a fast action search and localization method that supports relevance feedback from user. By characterizing videos as spatio-temporal interest points and building a random forest to index and match these points, our query matching is robust and efficient. To enable efficient action localization, we propose a coarse-to-fine subvolume search scheme, which is several orders faster than the existing video branch and bound search. The challenging cross-data search of several actions validates the effectiveness and efficiency of our method.
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