Human Pose Estimation Based on Secondary Generation Adversary

Xiankun Zhang; Rongfen Zhang; Yuhong Liu

doi:10.3788/LOP57.201509

Laser & Optoelectronics Progress, Volume. 57, Issue 20, 201509(2020)

Human Pose Estimation Based on Secondary Generation Adversary

Xiankun Zhang, Rongfen Zhang, and Yuhong Liu^*

Key Laboratory of Big Data and Intelligent Technology, College of Big Data and Information Engineering, Guizhou University, Guiyang, Guizhou 550025, China

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Abstract Get PDF(in Chinese)

Figures & Tables(16)

Fig. 1. Structure schematic diagram of our method

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Fig. 2. Schematic diagram of heatmap

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Fig. 3. Structure of hourglass network

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Fig. 4. Cascade structure diagram of SHN

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Fig. 5. Structure of intermediate supervision

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Fig. 6. Procedure of ASR

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Fig. 7. Procedure of AHO

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Fig. 8. Reconstruction of heatmap

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Fig. 9. Heatmaps obtained by different methods. (a) Ref. [4]; (b) Ref. [10]; (c) Ref. [11]; (d) ours

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Fig. 10. Comparison of joint estimation errors

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Table 1. Training process of batch images

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Table 1. Training process of batch images

Input: a mini-batch training image set X
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.

Table 2. Training process of single image

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Table 2. Training process of single image

Input: image x

1.Get shortcut features from D₁;2.Get distribution P from shortcut features in G₁;3.Sample an adversarial augmentation data

\overset{︵}{x}

from P;4.Compute the loss of D₁:

{\overset{︵}{L}}_{MSE}

with

\overset{︵}{x}

;5.Random augment x to get

\overset{︵}{x}

;6.Compute the loss of D₁:

{\overset{︵}{L}}_{MSE}

with

\overset{︵}{x}

;7.Compare

\overset{︵}{L}

and

\overset{︵}{L}

with formula (5) and formula (6) to update G₁;8.Update D₁.

Table 3. Training process of the secondary generation adversary

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Table 3. Training process of the secondary generation adversary

Input: image x;ground truth heatmap C
1. D₂ reconstructs heatmap: D(C,x);2. Compute L_real with formula (11);3. G₂ generates predictive heatmap: $\tilde{C}$ =G(x);4. Compute L_MSE with formula (8);5. D₂ reconstructs heatmap:D( $\tilde{C}$ ,x);6. Compute $p_{\tilde{C}}$ ;7. Compute L_fake、L '_D with formula (11)、formula (12);8. Update D₂;9. Compute L_adv、L_G with formula (9)、formula (10);10.Update G₂.

Table 4. PCK of different methods in LSP data setunit: %

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Table 4. PCK of different methods in LSP data setunit: %

Method	Head	Shoulder	Elbow	Wrist	Hip	Knee	Ankle	Mean
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

Table 5. PCKh of different methods in the MPII data setunit: %

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Table 5. PCKh of different methods in the MPII data setunit: %

Method	Head	Shoulder	Elbow	Wrist	Hip	Knee	Ankle	Mean
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

Table 6. Comparison of model efficiency
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Table 6. Comparison of model efficiency
Method Convergenceiteration times Average processingtime /s GFLOPs /(10⁹ times) Number ofparameters /10⁷
Ref. [11] 19500 0.48 10.820 5.495
Ours 26600 0.73 13.702 6.738

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Xiankun Zhang, Rongfen Zhang, Yuhong Liu. Human Pose Estimation Based on Secondary Generation Adversary[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201509

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