Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition

25 Mar 2013  ·  Andres Sanin, Conrad Sanderson, Mehrtash T. Harandi, Brian C. Lovell ·

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.

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Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Hand Gesture Recognition Cambridge Sanin et al. [sanin2013spatio] Accuracy 93% # 2


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