Spatio-temporal Shape and Flow Correlation for Action Recognition

This paper explores the use of volumetric features for action recognition. First, we propose a novel method to correlate spatio-temporal shapes to video clips that have been automatically segmented. Our method works on over-segmented videos, which means that we do not require background subtraction...

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Bibliographic Details
Main Authors: Yan Ke, Sukthankar, R., Hebert, M.
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computer science
Humans
Image analysis
Image motion analysis
Image recognition
Object recognition
Object segmentation
Robustness
Shape
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Summary:This paper explores the use of volumetric features for action recognition. First, we propose a novel method to correlate spatio-temporal shapes to video clips that have been automatically segmented. Our method works on over-segmented videos, which means that we do not require background subtraction for reliable object segmentation. Next, we discuss and demonstrate the complementary nature of shape- and flow-based features for action recognition. Our method, when combined with a recent flow-based correlation technique, can detect a wide range of actions in video, as demonstrated by results on a long tennis video. Although not specifically designed for whole-video classification, we also show that our method's performance is competitive with current action classification techniques on a standard video classification dataset.
ISSN:1063-6919
DOI:10.1109/CVPR.2007.383512