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Dynamic time warping-based features with class-specific joint importance maps for action recognition using kinect depth sensor

Mohammadzade, H ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/JSEN.2021.3051497
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. This paper proposes a novel 3D action recognition technique that uses time-series information extracted from depth image sequences for use in systems of human daily activity monitoring. To this end, each action is represented as a multi-dimensional time series, where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped onto a vector space using Dynamic Time Warping (DTW) distance. Furthermore, to employ the correlation-distinctiveness relationship of the sequences in recognition, this vector space is remapped onto a discriminative space using the regularized Fisher method, where final decisions about the actions are made. Unlike other available methods, the time-warping used in the mapping strategy makes the feature space robust to temporal variations of the motion sequences. Moreover, our method eliminates the need for a complicated design method for extracting the static and dynamic information of a motion sequence. Furthermore, most existing methods treat all skeletal joints identically for different actions, while some joints are more discriminative to distinguish a specific action. Thanks to the nature of the proposed features, we propose to use a separate set of discriminative joints, called joint importance map for each class of action. Evaluation results on four well-known datasets, TST, UTKinect, UCFKinect, and NTU RGB+D show competitive performance with the state-of-the-art methods in human action recognition. © 2001-2012 IEEE
  6. Keywords:
  7. Petroleum reservoir evaluation ; Time series ; Competitive performance ; Daily activity monitoring ; Dynamic information ; Dynamic time warping ; Evaluation results ; Human-action recognition ; State-of-the-art methods ; Time series informations ; Vector spaces
  8. Source: IEEE Sensors Journal ; Volume 21, Issue 7 , 2021 , Pages 9300-9313 ; 1530437X (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9321506