Spatio-temporal covariance descriptors for action and gesture recognition

We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to cr...

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Bibliographic Details
Main Authors: Sanin, A., Sanderson, C., Harandi, M. T., Lovell, B. C.
Format: Conference Proceeding
Language:English
Subjects:
Boosting
Classification
Computer vision
Covariance
Covariance matrix
Feature extraction
Manifolds
Projection
Recognition
Representations
Symmetric matrices
Tracking
Training
Vectors
Workshops
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Summary:We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions. We also show how the descriptors can be computed quickly through the use of integral video representations. Experiments on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets indicate superior performance of the proposed method compared to several recent state-of-the-art techniques. The proposed method is robust and does not require additional processing of the videos, such as foreground detection, interest-point detection or tracking.
ISSN:1550-5790
2642-9381
1550-5790
DOI:10.1109/WACV.2013.6475006