Human activity recognition in the semantic simplex of elementary actions

This paper presents an original approach for recognizing human activities in video sequences. A human activity is seen as a temporal sequence of elementary action probabilities. Actions are first generically learned using a robust action recognition method based on optical flow estimation and a cross-dataset training process. Activities are then projected as trajectories on the semantic simplex in order to be characterized and discriminated. A new trajectory attribute based on the total curvature Fourier descriptor is introduced. This attribute takes into account the induced geometry of the simplex manifold. Experiments on labelled datasets of human activities prove the efficiency of the proposed method for discriminating complex actions.