Author Affiliations
Key Laboratory of Big Data and Intelligent Technology, College of Big Data and Information Engineering, Guizhou University, Guiyang, Guizhou 550025, Chinashow less
Fig. 1. Structure schematic diagram of our method
Fig. 2. Schematic diagram of heatmap
Fig. 3. Structure of hourglass network
Fig. 4. Cascade structure diagram of SHN
Fig. 5. Structure of intermediate supervision
Fig. 6. Procedure of ASR
Fig. 7. Procedure of AHO
Fig. 8. Reconstruction of heatmap
Fig. 9. Heatmaps obtained by different methods. (a) Ref. [4]; (b) Ref. [10]; (c) Ref. [11]; (d) ours
Fig. 10. Comparison of joint estimation errors
Input: a mini-batch training image set X |
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1.X is randomly and equally divided into X1、X2、X3;2.Train D1 using X1;3.Train G1、D1 using X2 with table 2 on ASR;4.Train G1、D1 using X3 with table 2 on AHO. |
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Table 1. Training process of batch images
Input: image x |
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1.Get shortcut features from D1;2.Get distribution P from shortcut features in G1;3.Sample an adversarial augmentation data from P;4.Compute the loss of D1: with ;5.Random augment x to get ;6.Compute the loss of D1: with ;7.Compare and with formula (5) and formula (6) to update G1;8.Update D1. |
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Table 2. Training process of single image
Input: image x;ground truth heatmap C |
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1. D2 reconstructs heatmap: D(C,x);2. Compute Lreal with formula (11);3. G2 generates predictive heatmap: =G(x);4. Compute LMSE with formula (8);5. D2 reconstructs heatmap:D(,x);6. Compute ;7. Compute Lfake、L 'D with formula (11)、formula (12);8. Update D2;9. Compute Ladv、LG with formula (9)、formula (10);10.Update G2. |
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Table 3. Training process of the secondary generation adversary
Method | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean |
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Ref. [21] | 97.8 | 92.5 | 87.0 | 83.9 | 91.5 | 90.8 | 89.9 | 90.5 | Ref. [4] | 98.2 | 94.0 | 91.2 | 87.2 | 93.5 | 94.5 | 92.6 | 93.0 | Ref. [12] | 98.5 | 94.0 | 89.8 | 87.5 | 93.9 | 94.1 | 93.0 | 93.1 | Ref. [10] | 98.6 | 95.3 | 92.8 | 90.0 | 94.8 | 95.3 | 94.5 | 94.5 | Ref. [11] | 98.2 | 94.9 | 92.2 | 89.5 | 94.2 | 95.0 | 94.1 | 94.0 | Ours | 98.8 | 95.7 | 92.6 | 90.8 | 94.8 | 96.1 | 95.0 | 94.8 |
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Table 4. PCK of different methods in LSP data setunit: %
Method | Head | Shoulder | Elbow | Wrist | Hip | Knee | Ankle | Mean |
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Ref. [21] | 97.8 | 95.0 | 88.7 | 84.0 | 88.4 | 82.8 | 79.4 | 88.5 | Ref. [4] | 98.2 | 96.3 | 91.2 | 87.1 | 90.1 | 87.4 | 83.6 | 90.9 | Ref. [12] | 98.6 | 96.2 | 90.9 | 86.7 | 89.8 | 87.0 | 83.2 | 90.6 | Ref. [10] | 98.1 | 96.6 | 92.5 | 88.4 | 90.7 | 87.7 | 83.5 | 91.5 | Ref. [11] | 98.2 | 96.8 | 92.2 | 88.0 | 91.3 | 89.1 | 84.9 | 91.8 | Ours | 98.4 | 97.1 | 93.4 | 88.7 | 92.5 | 90.3 | 85.2 | 92.2 |
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Table 5. PCKh of different methods in the MPII data setunit: %
Method | Convergenceiteration times | Average processingtime /s | GFLOPs /(109 times) | Number ofparameters /107 |
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Ref. [11] | 19500 | 0.48 | 10.820 | 5.495 | Ours | 26600 | 0.73 | 13.702 | 6.738 |
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Table 6. Comparison of model efficiency