KaydedilenlerDerginizi ekleyin

Dergiler / Sakarya University Journal of Science / 2023 / Cilt: 27 Sayı: 5

Estimating Human Poses Using Deep Learning Model

Firgat MURADLİ
Serap ÇAKAR
Feyza CEREZCİ
Gülüzar ÇİT
Sayfa
1079–1087
Yayın Tarihi
2023-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The model's performance is evaluated based on the validation rate of the dataset and the accuracy of our model was 88.8 percent for VGG16 and 67 percent for ResNet50.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Over the past decade, extensive research has focused on the extraction of 3D human poses from images.
📊
Temel Bulgular & Veri:Deep learning models have gained widespread adoption and have demonstrated impressive performance across various domains of research and engineering.
💡
Sonuç & Katkı:The model's performance is evaluated based on the validation rate of the dataset and the accuracy of our model was 88.8 percent for VGG16 and 67 percent for ResNet50.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:2D human pose · convolutional neural network · transfer learning

Abstract

Over the past decade, extensive research has focused on the extraction of 3D human poses from images. The existing datasets must effectively address common challenges related to pose estimation. These datasets serve as valuable resources for evaluating, informing, and comparing different models. Deep learning models have gained widespread adoption and have demonstrated impressive performance across various domains of research and engineering. In this study, we employ these models, leveraging the open-source libraries OpenCV and Keras. To enhance the diversity and complexity of the training and testing process, we utilize the MPII Human Pose dataset. Specifically, we train and test the ResNet50 and VGG16 models using this dataset, resulting in significant improvements. The model's performance is evaluated based on the validation rate of the dataset and the accuracy of our model was 88.8 percent for VGG16 and 67 percent for ResNet50.

🔖

Bu Makaleye Atıf Yap

MURADLİ, F., ÇAKAR, S., CEREZCİ, F., ÇİT, G. (2023). Estimating Human Poses Using Deep Learning Model. Sakarya University Journal of Science, Cilt: 27 Sayı: 5, 1079–1087. https://ajindex.com/articles/970788

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (12)

  1. G. Pavlakos, X. Zhou, K. G. Derpanis, K. Daniilidis, "Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose", Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 1263–1272, 2017.
  2. M. Andriluka, L. Pishchulin, P. Gehler, B. Schiele, “2D Human Pose Estimation: New Benchmark and State of the Art Analysis”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3686–3693, 2014.
  3. H. Yasin, U. Iqbal, B. Kruger, A. Weber, J. Gall, "A Dual-Source Approach for 3D Pose Estimation from a Single Image", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4948–4956, 2016.
  4. P. F. Felzenszwalb, D. P. Huttenlocher, “Pictorial Structures for Object Recognition,” International Journal of Computer Vision, vol. 61, no. 1, pp. 55–79, 2005.
  5. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, "Going Deeper with Convolutions", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9, 2015.
  6. K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning For Image Recognition", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778, 2016.
  7. B. Tekin, A. Rozantsev, V. Lepetit, P. Fua, "Direct Prediction of 3D Body Poses from Motion Compensated Sequences", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 991–1000, 2016.
  8. G. Pavlakos, X. Zhou, K. Daniilidis, "Ordinal Depth Supervision for 3D Human Pose Estimation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7307-7316, 2018.
  9. H. Y. F. Tung, H. W. Tung, E. Yumer, K. Fragkiadaki, "Self-Supervised Learning of Motion Capture", Advances in Neural Information Processing Systems, pp. 5237–5247, 2017.
  10. X. Zhou, Q. Huang, X. Sun, X. Xue, Y. Wei, "Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach", Proceedings of the IEEE Conference on Computer Vision, pp. 398–407, 2017.
  11. A. Kanazawa, M. J. Black, D. W. Jacobs, J. Malik, "End-to-End Recovery of Human Shape and Pose", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7122–7131, 2018.
  12. T. L. Munea, Y. Jembre, H. Weldegebriel, L. Chen, C. Huang, C. Yang, "The Progress of Human Pose Estimation: A Survey and Taxonomy of Models Applied in 2D Human Pose Estimation", IEEE Acc