Unsupervised Random Forest Indexing for Fast Action Search

Despite recent successes of searching small object in images,
it remains a challenging problem to search and locate
actions in crowded videos 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 the 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-dataset search of several actions validates the effectiveness
and efficiency of our method.