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.

提出了一种新的基于时空协方差描述符和加权黎曼局部保留投影的动作和手势识别方法，并利用加权投影在整合时空区域的同时进行提升以创建最终的多类分类算法。通过积分视频表示法快速计算描述符。在 UCF 体育、CK + 面部表情和剑桥手势数据集上的实验表明，所提出的方法与几种最新技术相比具有卓越的性能。该方法具有鲁棒性，不需要对视频进行额外的处理，如前景检测、兴趣点检测或跟踪。