Real-Time Multi-Person Video-Based Pose Estimation

Fig. 4. Comparison of results in different scenarios for each model. (a)-(d) Scale change; (e)-(h) dense population; (i)-(l) occlusion; (m)-(p) complex pose

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Table 1. Algorithm model and environment configuration
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Table 1. Algorithm model and environment configuration
Model Framework Programminglanguage
CMU-Pose^[10] Caffe Python3.6.2
MaskR-CNN^[11] TensorFlow1.3.0+Keras2.2.6 Python3.6.2
RMPE^[14] Pytorch0.4.0 Python3.6.2
Proposed model Pytorch0.4.0 Python3.6.2

Table 2. Comparison of performance of each pose estimation model
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Table 2. Comparison of performance of each pose estimation model
Model AP AP_@0.5 AP_@0.75 AP_m AP_l
CMU-Pose 61.8 84.9 67.5 57.1 68.2
Mask R-CNN 63.1 87.3 68.7 57.8 71.4
RMPE 72.3 89.2 79.1 68.0 78.6
Proposed model 74.1 92.5 80.5 70.6 79.5

Table 3. Comparison of parameters of each human detection algorithm
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Table 3. Comparison of parameters of each human detection algorithm
Model Data set Running speed /(frame·s^-1) Parametersize /MB Calculatedamount /10⁹
YOLOv3^[15] MS COCO 51 237 65.86
Proposed model MS COCO 64 195 44.32

Table 4. AP-AR values of model under different inputs
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Table 4. AP-AR values of model under different inputs
Input AP AP_@0.5 AP_@0.75 AP_m AP_l AR AR_@0.5 AR_@0.75 AR_m AR_l
256 pixel×192 pixel 71.2 91.4 78.3 68.5 75.2 74.3 92.2 80.9 71.3 78.9
384 pixel×288 pixel 74.1 92.5 80.5 70.6 79.5 76.8 93.2 82.5 73.0 82.6

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Yan Fenting, Wang Peng, Lü Zhigang, Ding Zhe, Qiao Mengyu. Real-Time Multi-Person Video-Based Pose Estimation[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21006

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