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Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition.

Chao Tang1,2, Anyang Tong1,2, Aihua Zheng2

  • 1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

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This study introduces a new human action recognition (HAR) method using RGB-Depth data and a selective ensemble support vector machine (SESVM). The approach enhances accuracy and robustness for effective action recognition.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Traditional human action recognition (HAR) relies on RGB video, which has limitations in accuracy and robustness.
  • RGB-Depth (RGB-D) data, captured by sensors like Microsoft Kinect, offers improved performance for HAR.
  • There is a growing need for robust and accurate HAR systems in various industries.

Purpose of the Study:

  • To propose a novel multimodal feature fusion method for human action recognition.
  • To develop a selective ensemble support vector machine (SESVM) classification model for enhanced HAR.
  • To demonstrate the efficiency and effectiveness of the proposed method compared to existing algorithms.

Main Methods:

  • Fusion of improved HOG features from RGB data, depth motion map-based local binary pattern (DMM-LBP) features, and hybrid joint features (HJF).
  • Implementation of a frame-based selective ensemble support vector machine (SESVM) classification model.
  • Integration of selective ensemble strategy with SVM base classifier selection to maximize classifier diversity.

Main Results:

  • The proposed method achieves high accuracy and robustness in human action recognition tasks.
  • Experimental results on public datasets show the method is simple, fast, and efficient.
  • The SESVM model effectively integrates multimodal features, outperforming other action recognition algorithms.

Conclusions:

  • The proposed multimodal feature fusion approach combined with SESVM significantly improves human action recognition.
  • The method offers a practical and efficient solution for real-world HAR applications.
  • Further research can explore advanced feature extraction and ensemble strategies for HAR.