SkelResNet: Transfer Learning Approach for Skeleton-Based Action Recognition

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dc.contributor.authorKilic, Ugur
dc.contributor.authorKaradag, Ozge Oztimur
dc.contributor.authorOzyer, Gulsah Tumuklu
dc.date.accessioned2026-01-24T12:29:01Z
dc.date.available2026-01-24T12:29:01Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
dc.description.abstractSkeleton-based action recognition is an increasingly popular research area in computer vision that analyzes the spatial configuration and temporal dynamics of human action. Learning distinctive spatial and temporal features for skeleton-based action recognition is one of the main challenges in this field. For this purpose, various deep learning methods such as CNN, RNN, GCN and Transformer have been used in the literature. Although these methods can achieve high performance, they require high computational costs and large datasets due to their complexity. Transfer learning is an approach that can be used to overcome this problem. In transfer learning, a pre-trained model can be fine-tuned for a new task. In this way, the computational cost can be reduced and high performance can be achieved with less data. In this study, SkelResNet architecture is designed based on the pre-trained ResNet101 model. Four different image representations were created using skeletal data to meet the input requirements of the SkelResNet architecture. Experimental studies have shown that SkelResNet outperforms CNN-based methods in the existing literature in action recognition.
dc.description.sponsorshipIEEE,IEEE Turkey,Koluman & Berdan,Loodos,Figes,Turkcell,Yildirim Elect
dc.identifier.doi10.1109/SIU61531.2024.10601052
dc.identifier.isbn979-8-3503-8897-8
dc.identifier.isbn979-8-3503-8896-1
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85200914538
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10601052
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5071
dc.identifier.wosWOS:001297894700264
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof32nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260121
dc.subjectaction recognition
dc.subjectskeleton sequence
dc.subjectconvolutional neural network
dc.subjecttransfer learning
dc.titleSkelResNet: Transfer Learning Approach for Skeleton-Based Action Recognition
dc.title.alternativeSkelResNet: İskelet Tabanlı Eylem Tanıma için Transfer Öğrenme Yaklaşımı
dc.typeConference Object

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