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An integrated multi-person pose estimation and activity recognition technique using 3D dual network

Author

Listed:
  • Ishita Arora

    (AIACTR Affiliated to GGSIPU)

  • M. Gangadharappa

    (NSUT East Campus Affiliated to GGSIPU)

Abstract

Human pose estimation and detection are critical for understanding human activities in videos and images. This paper presents a novel approach to meet the advanced demands of human–computer interactions and assisted living systems through enhanced human pose estimation and activity recognition. We introduce IMPos-DNet, an innovative technique that integrates multi-person pose estimation and activity recognition using a 3D Dual Convolution Neural Network (CNN) applied to multiview video datasets. Our approach combines top-down and bottom-up models to improve performance. The top-down network focuses on evaluating human joints for each individual, enhancing robustness against inaccurate bounding boxes, while the bottom-up network employs normalized heatmaps based on human detection, improving resilience to scale variation. By synergizing the 3D poses estimated by both networks, IMPos-DNet produces precise final 3D poses. Our research objectives include advancing the accuracy and efficiency of pose estimation and activity recognition, as well as addressing the scarcity of 3D ground-truth data. To this end, we employ a semi-supervised method, broadening the model’s applicability. Comprehensive experiments on three publicly available datasets—Human3.6 M, MuPoTs-3D, and MPI-INF-3DHP—demonstrate the model’s superior accuracy and efficiency. Evaluation results confirm the effectiveness of IMPos-DNet’s individual components, highlighting its potential for reliable human pose estimation and activity recognition.

Suggested Citation

  • Ishita Arora & M. Gangadharappa, 2025. "An integrated multi-person pose estimation and activity recognition technique using 3D dual network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(2), pages 667-684, February.
  • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:2:d:10.1007_s13198-024-02640-0
    DOI: 10.1007/s13198-024-02640-0
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