Skeleton-based human action recognition using transformer model with softmax with multidimensional connected weights

Marakhimov, Avazjon, Khudaybergenov, Kabul

Innovations in Science and Technologies · 2025-yil

Annotatsiya

Skeleton-based human action recognition (HAR), particularly from CCTV surveillance footage, has garnered significant interest within the artificial intelligence community. The skeletal modality provides a robust, high-level representation of human motion. Prevailing methods in this domain predominantly rely on a joint-centric approach, modeling the human body as a set of coordinate points. However, this representation often fails to fully capture the rich structural and kinematic relationships essential for accurate motion classification. To address this limitation, we propose a novel method termed SoftMax with Multi-Dimensional Connected Weights. This approach enhances classification by explicitly modeling the informative connections between body joints, represented as skeletal edges. We develop an end-to-end deep learning framework that learns discriminative spatio-temporal representations directly from sequences of skeleton point vectors using Convolutional Neural Networks (CNNs). Results demonstrate that our approach achieves stateof-the-art performance, underscoring the effectiveness of leveraging skeletal edge information and advanced classification techniques for human action recognition.

Maqola ma’lumotlari
MualliflarMarakhimov, Avazjon, Khudaybergenov, Kabul
JurnalInnovations in Science and Technologies
NashriyotТошкент давлат иқтисодиёт университети
ISSN3030-3451
Nashr sanasi2025-08-27
Jild2
Son7
Betlar415-422
TilIngliz
Ilmiy sohaIqtisodiyot
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

SoftMax, machine learning, action classification, skeleton motion, human action recognition, convolution, deep learning.

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APAMarakhimov, Avazjon, & Khudaybergenov, Kabul (2025). SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS. Innovations in Science and Technologies, 2(7), 415-422. https://innoist.uz/index.php/ist/article/view/1213
GOST (OAK)Marakhimov, Avazjon, Khudaybergenov, Kabul SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS // Innovations in Science and Technologies. – 2025. – T. 2, № 7. – P. 415-422.
MLAMarakhimov, Avazjon, and Khudaybergenov, Kabul. “SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS.” Innovations in Science and Technologies, 2(7), 415-422, 2025. https://innoist.uz/index.php/ist/article/view/1213
ChicagoMarakhimov, Avazjon, and Khudaybergenov, Kabul. “SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS.” Innovations in Science and Technologies, 2(7), 415-422 (2025). https://innoist.uz/index.php/ist/article/view/1213
HarvardMarakhimov, Avazjon, and Khudaybergenov, Kabul (2025) ‘SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS’, Innovations in Science and Technologies, 2(7), 415-422. https://innoist.uz/index.php/ist/article/view/1213
BibTeX
@article{marakhimovavazjon2025,
  title = {SKELETON-BASED HUMAN ACTION RECOGNITION USING TRANSFORMER MODEL WITH SOFTMAX WITH MULTIDIMENSIONAL CONNECTED WEIGHTS},
  author = {Marakhimov, Avazjon and Khudaybergenov, Kabul},
  journal = {Innovations in Science and Technologies},
  year = {2025},
  volume = {2},
  number = {7},
  pages = {415-422},
  url = {https://innoist.uz/index.php/ist/article/view/1213}
}

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