Real-time Human Action Search using Random Forest based Hough Voting

Many existing techniques in content based video retrieval

treat a video sequence as a whole to match it against a query

video or to assign a text label. Such an approach has serious

limitations when applied to human action retrieval because

an action may occur only in a sub-region and last for a small

portion of the video length. In situations like this, we essen-

tially need to match the subvolumes of the video sequences

against the query video. A naive exhaustive search is im-

practical due to large number of possible subvolumes for each

video sequence. In this paper, we propose a novel framework

for action retrieval which performs pattern matching at sub-

volume level and is very efficient in handling large corpus of

videos. We construct an unsupervised random forest to in-

dex the video database, generate a score volume with Hough

voting and then employ a max sub-path strategy to quickly

search for the temporal and spatial positions of all the video

sequences in the database. We present action search experi-

ments on challenging datasets to validate the efficiency and

effectiveness of our system.

PDF file

In  Multimeda (ACMMM)

Publisher  ACM
© 2012 ACM. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the ACM.


> Publications > Real-time Human Action Search using Random Forest based Hough Voting