PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations
Nozha Jlidi, Ahmed Snoun, Tahani Bouchrika, Olfa Jemai, Mourad Zaied · 2020
This paper introduces a novel approach for human activities recognition (HAR) based on body articulations (joints) that represent the connection between bones in the human body which join the skeletal system such as the knee, shoulder and hand, and which are made to allow different degrees and types of movement. To implement our system, we used PoseNet to extract articulation points, which will be classified employing transfer learning approach to recognize the activity. The created system will be named in the rest of the paper (PTLHAR). The experimental results show that the proposed approach provides a significant improvement over state-of-the-art methods.