ETRI Journal

  • 제42권1호
  • /
  • Pages.78-89
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  • 2020
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  • 1225-6463(pISSN)
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  • 2233-7326(eISSN)
한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Human activity recognition with analysis of angles between skeletal joints using a RGB-depth sensor

  • Ince, Omer Faruk (Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology) ;
  • Ince, Ibrahim Furkan (Department of Electronics Engineering, Kyungsung University) ;
  • Yildirim, Mustafa Eren (Department of Electronics Engineering, Kyungsung University) ;
  • Park, Jang Sik (Department of Electronics Engineering, Kyungsung University) ;
  • Song, Jong Kwan (Department of Electronics Engineering, Kyungsung University) ;
  • Yoon, Byung Woo (Department of Electronics Engineering, Kyungsung University)
  • 투고 : 2018.10.24
  • 심사 : 2019.07.29
  • 발행 : 2020.02.07
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초록

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

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피인용 문헌

  1. Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates vol.10, pp.23, 2020, https://doi.org/10.3390/app10238474
  2. Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System vol.13, pp.2, 2020, https://doi.org/10.3390/su13020970