IEEE Access (Jan 2019)

Combining Adaptive Hierarchical Depth Motion Maps With Skeletal Joints for Human Action Recognition

  • Runwei Ding,
  • Qinqin He,
  • Hong Liu,
  • Mengyuan Liu

DOI
https://doi.org/10.1109/ACCESS.2018.2886362
Journal volume & issue
Vol. 7
pp. 5597 – 5608

Abstract

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This paper presents a new framework for human action recognition by fusing human motion with skeletal joints. First, adaptive hierarchical depth motion maps (AH-DMMs) are proposed to capture the shape and motion cues of action sequences. Specifically, AH-DMMs are calculated over adaptive hierarchical windows and Gabor filters are used to encode the texture information of AH-DMMs. Then, spatial distances of skeletal joint positions are computed to characterize the structure information of the human body. Finally, two types of fusion methods including feature-level fusion and decision-level fusion are employed to combine the motion cues and structure information. The experimental results on public benchmark datasets, i.e., MSRAction3D and UTKinect-Action, show the effectiveness of the proposed method.

Keywords