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.
@article{arxiv.1303.6021,
title = {Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition},
author = {Andres Sanin and Conrad Sanderson and Mehrtash T. Harandi and Brian C. Lovell},
journal= {arXiv preprint arXiv:1303.6021},
year = {2013}
}