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.
Sanin et al. REF , proposed an action and gesture recognition method from videos based on spatio-temporal covariance descriptors.
Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
IEEE Workshop on Applications of Computer Vision, pp. 103-110, 2013
Computer Science