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.

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Publisher  IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
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