Junsong Yuan, Zicheng Liu, and Ying Wu
22 June 2009
Actions are spatio-temporal patterns which can be characterized
by collections of spatio-temporal invariant features.
Detection of actions is to find the re-occurrences
(e.g. through pattern matching) of such spatio-temporal
patterns. This paper addresses two critical issues in pattern
matching-based action detection: (1) efficiency of pattern
search in 3D videos and (2) tolerance of intra-pattern
variations of actions. Our contributions are two-fold. First,
we propose a discriminative pattern matching called naive-
Bayes based mutual information maximization (NBMIM)
for multi-class action categorization. It improves the stateof-
the-art results on standard KTH dataset. Second, a novel
search algorithm is proposed to locate the optimal subvolume
in the 3D video space for efficient action detection.
Our method is purely data-driven and does not rely on object
detection, tracking or background subtraction. It can
well handle the intra-pattern variations of actions such as
scale and speed variations, and is insensitive to dynamic
and clutter backgrounds and even partial occlusions. The
experiments on versatile datasets including KTH and CMU
action datasets demonstrate the effectiveness and efficiency
of our method.
|Published in||IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, June 22-24, 2009.|
|Publisher||IEEE Computer Society|
Copyright © 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or 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 must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.