Discriminative Subvolume Search for Efficient Action Detection

Junsong Yuan, Zicheng Liu, and Ying Wu

Abstract

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.

Details

Publication typeProceedings
Published inIEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), Miami, Florida, June 22-24, 2009.
PublisherIEEE Computer Society
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