PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations

Nozha Jlidi; Ahmed Snoun; Tahani Bouchrika; Olfa Jemai; Mourad Zaied

doi:10.1117/12.2559567

31 January 2020 PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations

Nozha Jlidi, Ahmed Snoun, Tahani Bouchrika, Olfa Jemai, Mourad Zaied

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330Q (2020) https://doi.org/10.1117/12.2559567
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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.

Citation Download Citation

Nozha Jlidi, Ahmed Snoun, Tahani Bouchrika, Olfa Jemai, and Mourad Zaied "PTLHAR: PoseNet and transfer learning for human activities recognition based on body articulations", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330Q (31 January 2020); https://doi.org/10.1117/12.2559567

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