Skeleton-based human action recognition using spatio-temporal latent features with a gcn model

Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Кимёвий технология. Назорат ва бошқарув · 2025-yil

Annotatsiya

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates the stability and invariance characteristics of the LFHAR architecture. The method produces substantial performance improvements, achieving accuracy increases of 2.7% on dataset NTU-RGB+D-60-classes and 2.1% on dataset NTU-RGB+D-120-classes, confirming its accuracy in improving human action recognition.

Maqola ma’lumotlari
MualliflarAvazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin
JurnalКимёвий технология. Назорат ва бошқарув
NashriyotТошкент давлат техника университети, «Ўзқурилишматериаллар» ДАК, «Ўзкимёсаноат» ДАК, «Совпластитал» ҚҚ, Ўзбекистон Интеллектуал мулк агентлиги
ISSN2181-1105, 1815-4840
Nashr sanasi2025-12-28
Jild2025
Son6
Betlar103-109
TilIngliz
DOI10.59048/2181-1105.1725
Indeksga qo‘shilgan2026-09-02

DOI: 10.59048/2181-1105.1725 · Maqolaning asl sahifasi

Iqtibos keltirish

APAAvazjon Marakhimov, Kabul Khudaybergenov, & Mominov Zakhriddin (2025). SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL. Кимёвий технология. Назорат ва бошқарув, 2025(6), 103-109. https://doi.org/10.59048/2181-1105.1725
GOST (OAK)Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL // Кимёвий технология. Назорат ва бошқарув. – 2025. – T. 2025, № 6. – P. 103-109. – DOI: 10.59048/2181-1105.1725.
MLAAvazjon Marakhimov, Kabul Khudaybergenov, and Mominov Zakhriddin. “SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL.” Кимёвий технология. Назорат ва бошқарув, 2025(6), 103-109, 2025. https://doi.org/10.59048/2181-1105.1725
ChicagoAvazjon Marakhimov, Kabul Khudaybergenov, and Mominov Zakhriddin. “SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL.” Кимёвий технология. Назорат ва бошқарув, 2025(6), 103-109 (2025). https://doi.org/10.59048/2181-1105.1725
HarvardAvazjon Marakhimov, Kabul Khudaybergenov, and Mominov Zakhriddin (2025) ‘SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL’, Кимёвий технология. Назорат ва бошқарув, 2025(6), 103-109. https://doi.org/10.59048/2181-1105.1725
BibTeX
@article{avazjonmarakhimov2025,
  title = {SKELETON-BASED HUMAN ACTION RECOGNITION USING SPATIO-TEMPORAL LATENT FEATURES WITH A GCN MODEL},
  author = {Avazjon Marakhimov and Kabul Khudaybergenov and Mominov Zakhriddin},
  journal = {Кимёвий технология. Назорат ва бошқарув},
  year = {2025},
  volume = {2025},
  number = {6},
  pages = {103-109},
  doi = {10.59048/2181-1105.1725},
  url = {https://doi.org/10.59048/2181-1105.1725}
}

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