Spatio-temporal gcn with softmax classifier for skeleton-based human action recognition

Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Кимёвий технология. Назорат ва бошқарув · 2025-yil

Annotatsiya

Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features into spatio-temporal matrices. We tested LFHAR on standard datasets to demonstrate its stability and effectiveness. Our method shows significant improvements, achieving 2.7% higher accuracy on NTU-RGB+D 60 and 2.1% higher accuracy on NTU-RGB+D 120 compared to baseline methods. These results confirm that the LFHAR framework effectively improves skeleton-based action recognition performance.

Maqola ma’lumotlari
MualliflarKabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov
JurnalКимёвий технология. Назорат ва бошқарув
NashriyotТошкент давлат техника университети, «Ўзқурилишматериаллар» ДАК, «Ўзкимёсаноат» ДАК, «Совпластитал» ҚҚ, Ўзбекистон Интеллектуал мулк агентлиги
ISSN2181-1105, 1815-4840
Nashr sanasi2025-11-01
Jild2025
Son5
Betlar88-95
TilIngliz
DOI10.59048/2181-1105.1724
Indeksga qo‘shilgan2026-09-02

DOI: 10.59048/2181-1105.1724 · Maqolaning asl sahifasi

Iqtibos keltirish

APAKabul Khudaybergenov, Avazjon Marakhimov, & Zahriddin Muminov (2025). SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION. Кимёвий технология. Назорат ва бошқарув, 2025(5), 88-95. https://doi.org/10.59048/2181-1105.1724
GOST (OAK)Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION // Кимёвий технология. Назорат ва бошқарув. – 2025. – T. 2025, № 5. – P. 88-95. – DOI: 10.59048/2181-1105.1724.
MLAKabul Khudaybergenov, Avazjon Marakhimov, and Zahriddin Muminov. “SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION.” Кимёвий технология. Назорат ва бошқарув, 2025(5), 88-95, 2025. https://doi.org/10.59048/2181-1105.1724
ChicagoKabul Khudaybergenov, Avazjon Marakhimov, and Zahriddin Muminov. “SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION.” Кимёвий технология. Назорат ва бошқарув, 2025(5), 88-95 (2025). https://doi.org/10.59048/2181-1105.1724
HarvardKabul Khudaybergenov, Avazjon Marakhimov, and Zahriddin Muminov (2025) ‘SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION’, Кимёвий технология. Назорат ва бошқарув, 2025(5), 88-95. https://doi.org/10.59048/2181-1105.1724
BibTeX
@article{kabulkhudaybergenov2025,
  title = {SPATIO-TEMPORAL GCN WITH SOFTMAX CLASSIFIER FOR SKELETON-BASED HUMAN ACTION RECOGNITION},
  author = {Kabul Khudaybergenov and Avazjon Marakhimov and Zahriddin Muminov},
  journal = {Кимёвий технология. Назорат ва бошқарув},
  year = {2025},
  volume = {2025},
  number = {5},
  pages = {88-95},
  doi = {10.59048/2181-1105.1724},
  url = {https://doi.org/10.59048/2181-1105.1724}
}

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