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Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

Author

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  • Ahmad Jalal

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Israr Akhtar

    (Department of Computer Science, Air University, Islamabad 44000, Pakistan)

  • Kibum Kim

    (Department of Human-Computer Interaction, Hanyang University, Ansan 15588, Korea)

Abstract

This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods.

Suggested Citation

  • Ahmad Jalal & Israr Akhtar & Kibum Kim, 2020. "Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing," Sustainability, MDPI, vol. 12(23), pages 1-24, November.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:23:p:9814-:d:450254
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    References listed on IDEAS

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    1. Negar Golestani & Mahta Moghaddam, 2020. "Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    Citations

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    Cited by:

    1. Nida Khalid & Munkhjargal Gochoo & Ahmad Jalal & Kibum Kim, 2021. "Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System," Sustainability, MDPI, vol. 13(2), pages 1-30, January.
    2. Mahwish Pervaiz & Yazeed Yasin Ghadi & Munkhjargal Gochoo & Ahmad Jalal & Shaharyar Kamal & Dong-Seong Kim, 2021. "A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    3. Madiha Javeed & Munkhjargal Gochoo & Ahmad Jalal & Kibum Kim, 2021. "HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    4. Hira Ansar & Ahmad Jalal & Munkhjargal Gochoo & Kibum Kim, 2021. "Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities," Sustainability, MDPI, vol. 13(5), pages 1-26, March.
    5. Jiacheng Wu & Han Cui & Naim Dahnoun, 2023. "A Voxelization Algorithm for Reconstructing mmWave Radar Point Cloud and an Application on Posture Classification for Low Energy Consumption Platform," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    6. Agnieszka Dudziak & Monika Stoma & Emilia Osmólska, 2023. "Analysis of Consumer Behaviour in the Context of the Place of Purchasing Food Products with Particular Emphasis on Local Products," IJERPH, MDPI, vol. 20(3), pages 1-23, January.

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