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DMMs-Based Multiple Features Fusion for Human Action Recognition

The emerging cost-effective depth sensors have facilitated the action recognition task significantly. In this paper, the authors address the action recognition problem using depth video sequences combining three discriminative features. More specifically, the authors generate three Depth Motion Maps DMMs over the entire video sequence corresponding to the front, side, and top projection views. Contourlet-based Histogram of Oriented Gradients CT-HOG, Local Binary Patterns LBP, and Edge Oriented Histograms EOH are then computed from the DMMs. To merge these features, the authors consider decision-level fusion, where a soft decision-fusion rule, Logarithmic Opinion Pool LOGP, is used to combine the classification outcomes from multiple classifiers each with an individual set of features. Experimental results on two datasets reveal that the fusion scheme achieves superior action recognition performance over the situations when using each feature individually.