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DMMs-Based Multiple Features Fusion for Human Action Recognition

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  • Mohammad Farhad Bulbul

    (Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China)

  • Yunsheng Jiang

    (Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China)

  • Jinwen Ma

    (Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing, China)

Abstract

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.

Suggested Citation

  • Mohammad Farhad Bulbul & Yunsheng Jiang & Jinwen Ma, 2015. "DMMs-Based Multiple Features Fusion for Human Action Recognition," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 6(4), pages 23-39, October.
    • Handle: RePEc:igg:jmdem0:v:6:y:2015:i:4:p:23-39
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