A multi-modal gesture recognition system using audio, video, and skeletal joint data
Proceedings of the 15th ACM on International conference on multimodal interaction
Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.