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Journals / Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi / 2024 / Cilt: 7 Sayı: 2

Comparison of Deep Learning Based 2D Human Pose Estimation Models

Derin Öğrenmeye Dayalı 2 Boyutlu İnsan Poz Tahmin Modellerinin Karşılaştırılması

Cumhur Torun(KASTAMONU ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ)0009-0000-9984-1384
Abdulkadir Karacı(SAMSUN ÜNİVERSİTESİ)0000-0002-2430-1372
Pages
185–196
Publication Date
2024-12-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Human motion analysis is a field of study of great importance in computer vision and artificial intelligence.
📊
Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:İnsan hareket analizi · eklem noktası tespiti · bilgisayarlı görü

Abstract

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.

Özet

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.

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Cite This Article

Torun, C., Karacı, A. (2024). Comparison of Deep Learning Based 2D Human Pose Estimation Models. Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, Cilt: 7 Sayı: 2, 185–196. https://doi.org/10.51764/smutgd.1573626

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