Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition

Abstract

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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• Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
• Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
• Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
• Multi-Shot Person Re-Identification via Relational Stein Divergence
• Summarisation of Short-Term and Long-Term Videos using Texture and Colour