Comparison of Deep Learning Based 2D Human Pose Estimation Models

Yıl 2024, Cilt: 7 Sayı: 2, 185 - 196, 14.12.2024

Cumhur Torun , Abdulkadir Karacı

https://doi.org/10.51764/smutgd.1573626

Öz

Human motion analysis is a field of study of great importance in computer vision and artificial intelligence. In particular, the detection of joint points plays a critical role in digitally modeling human movements and postures. This field has many applications in many disciplines such as medicine, sports, rehabilitation, security, and human-computer interaction. By accurately and efficiently identifying joint points, it is possible to evaluate athletes' performance, monitor patients' rehabilitation process, and recognize complex gestures such as sign language in a digital environment. Various software libraries developed to detect joint points offer advantages in terms of speed, accuracy, and ease of use by using different algorithms. Popular libraries such as MediaPipe, MoveNet, OpenPose, AlphaPose, Detectron2, and HRNet are widely used in this field, and each has certain advantages and disadvantages. In this study, libraries are compared and compared for analyzing human movement. evaluations of the methods used and the areas where they can be used It was done. MediaPipe Holistic and MoveNet libraries were found to be successful in real-time applications, whereas AlphaPose, ViTPose, and HRNet were found to be more effective for applications requiring high accuracy.

Anahtar Kelimeler

Human motion analysis, joint point detection, computer vision, pose estimation models

Derin Öğrenmeye Dayalı 2 Boyutlu İnsan Poz Tahmin Modellerinin Karşılaştırılması

Öz

İnsan hareketlerinin analizi, bilgisayarlı görü ve yapay zekâ alanlarında büyük öneme sahip bir çalışma alanıdır. Özellikle eklem noktalarının tespiti, insan hareketlerinin ve duruşlarının dijital ortamda modellenmesi açısından kritik rol oynar. Bu alan, tıp, spor, rehabilitasyon, güvenlik, insan-bilgisayar etkileşimi gibi birçok disiplinde geniş bir kullanım alanına sahiptir. Eklem noktalarının doğru ve etkin bir şekilde belirlenmesi sayesinde, sporcuların performans değerlendirilmesi, hastaların rehabilitasyon süreçlerinin izlenmesi ve işaret dili gibi karmaşık hareketlerin dijital ortamda tanınması sağlanabilmektedir. Eklem noktalarını tespit etmek için geliştirilen çeşitli yazılım kütüphaneleri, farklı algoritmalar kullanarak hız, doğruluk ve kullanım kolaylığı açısından avantajlar sunmaktadır. MediaPipe, MoveNet, OpenPose, AlphaPose, Detectron2 ve HRNet gibi popüler kütüphaneler, bu alanda yaygın olarak kullanılmakta ve her birinin belirli avantaj ve dezavantajları bulunmaktadır. Bu çalışmada, kütüphaneler karşılaştırılarak insan hareketlerinin analizinde kullanılan yöntemler ve kullanılabilecekleri alanlara yönelik değerlendirmeler yapılmıştır. MediaPipe Holistic ve MoveNet kütüphaneleri gerçek zamanlı uygulamalarda başarılı bulunurken, AlphaPose, ViTPose ve HRNet'in yüksek doğruluk gerektiren uygulamalar için daha etkili olduğu görülmüştür.

Anahtar Kelimeler

İnsan hareket analizi, eklem noktası tespiti, bilgisayarlı görü, poz tahmin modelleri

Etik Beyan

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun davrandığımı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak gösterdiğimi ve bu kaynaklara kaynakçada yer verdiğimi; kullanılan verilerde herhangi bir değişiklik yapmadığımı, çalışmanın Committee on Publication Ethics (COPE)' in tüm şartlarını ve koşullarını kabul ederek etik görev ve sorumluluklara riayet ettiğimi beyan ederim. Herhangi bir zamanda, çalışmayla ilgili yaptığım bu beyana aykırı bir durumun saptanması durumunda, ortaya çıkacak tüm ahlaki ve hukuki sonuçlara razı olduğumu bildiririm.

Destekleyen Kurum

TÜBİTAK

Teşekkür

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) tarafından 124E379 numaralı hibe kapsamında desteklenmiştir. Yazarlar destekleri için TÜBİTAK'a teşekkürlerini sunarlar.

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