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

Ishita Arora () and M. Gangadharappa ()
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Ishita Arora: AIACTR Affiliated to GGSIPU
M. Gangadharappa: NSUT East Campus Affiliated to GGSIPU

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 2, No 14, 667-684

Abstract: 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.

Keywords: Artificial intelligence; Convolution Neural Network; Human centric system; Intelligent system; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-024-02640-0

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