A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images
| Autor: | Huynh The Vu, Richardt H. Wilkinson, Margaret Lech, Eva Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | IEEE Access, Vol 8, Pp 52830-52840 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2169-3536 65606337 |
| DOI: | 10.1109/ACCESS.2020.2980565 |
| Popis: | This paper investigates the problem of human pose estimation (HPE) from single 2-dimensional (2D) still images using a convolutional neural network (CNN). The aim was to train the CNN to analyze a 2D input image of a person to determine the person's pose. The CNN output was given in the form of a tree-structured graph of interconnected nodes representing 2D image coordinates of the person's body joints. A new data-driven tree-based model for HPE was validated and compared to the traditional anatomy-based tree-based structures. The effect of the number of nodes in anatomy-based tree-based structures on the accuracy of HPE was examined. The tree-based techniques were compared with non-tree-based methods using a common HPE framework and a benchmark dataset. As a result of this investigation, a new hybrid two-stage approach to the HPE estimation was proposed. In the first stage, a non-tree-based network was used to generate approximate results that were then passed for further refinement to the second, tree-based stage. Experimental results showed that both of the proposed methods, the data-driven tree-based model (TD_26) and the hybrid model (H_26_2B) lead to very similar results, obtaining 1% higher HPE accuracy compared to the benchmark anatomy-based model (TA_26) and 3% higher accuracy compared to the non-tree-based benchmark (NT_26_A). The best overall HPE results were obtained using the anatomy-based benchmark with the number of nodes increased from 26 to 50, which also significantly increased the computational cost. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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