# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 4 Experiments and Results
### Results and Analysis  
To evaluate our method, we compared it with current state-of-the-art lightweight hourglass network methods. The authors of [8] proposed a new hourglass architecture using hierarchical residual blocks and evaluated the performance of network binarization in human pose estimation. Single-stack and an eight-stack networks are implemented for comparison.  
As shown in Table 5, our method enhanced performance in pose estimation, despite an approximately 40% reduction in the number of parameters, as compared to state-of-the-art lightweight hourglass networks. It can be seen that the human pose estimation performance using our method was superior.  
We compared our results with those of existing methods on the MPII and LSP datasets. Table 6 presents the PCKh scores from different methods with the MPII dataset. Table 7 shows the PCK scores with the LSP dataset. The results confirmed that the proposed method shows improved human pose performance compared to the existing methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Head** & **Sho.** & **Elb.** & **Wri.** & **Hip** & **Knee** & **Ank.** & **Mean** \\ \hline [PERSON] et al. [39] & 74.3 & 49.0 & 40.8 & 34.1 & 36.5 & 34.4 & 35.2 & 44.1 \\ [PERSON] et al. [3] & 95.8 & 90.3 & 80.5 & 74.3 & 77.6 & 69.7 & 62.8 & 79.6 \\ [PERSON] et al. [40] & 95.7 & 91.7 & 81.7 & 72.4 & 82.8 & 73.2 & 66.4 & 81.3 \\ [PERSON] et al. [41] & 96.1 & 91.9 & 83.9 & 77.8 & 80.9 & 72.3 & 64.8 & 82.0 \\ [PERSON] et al. [42] & 95.0 & 91.6 & 83.0 & 76.6 & 81.9 & 74.5 & 69.5 & 82.4 \\ [PERSON] et al. [43] & 94.1 & 90.2 & 83.4 & 77.3 & 82.6 & 75.7 & 68.6 & 82.4 \\ [PERSON] et al. [44] & 97.8 & 93.3 & 85.7 & 80.4 & 85.3 & 76.6 & 70.2 & 85.0 \\ [PERSON] et al. [45] & 96.2 & 93.1 & 86.7 & 82.1 & 85.2 & 81.4 & 74.1 & 86.1 \\ [PERSON] et al. [46] & 97.2 & 93.9 & 86.4 & 81.3 & 86.8 & 80.6 & 73.4 & 86.3 \\ [PERSON] et al. [24] & 97.7 & 95.0 & 88.2 & 83.0 & 87.9 & 82.6 & 78.4 & 88.1 \\ [PERSON] et al. [47] & 96.8 & 95.2 & 89.3 & 84.4 & 88.4 & 83.4 & 78.0 & 88.5 \\ [PERSON] et al. [15] & 97.8 & 95.0 & 88.7 & 84.0 & 88.4 & 82.8 & 79.4 & 88.5 \\ [PERSON] et al. [8] & 97.9 & 95.1 & 89.9 & 85.3 & 89.4 & 85.7 & 81.7 & 89.7 \\ Ours & 98.1 & 96.2 & 90.9 & 87.2 & 89.8 & 87.3 & 83.5 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy comparison with existing methods using the MPII validation dataset ([EMAIL_ADDRESS]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[8]-Real** & **[8]-Real** & **Ours** & **Ours** \\ \([\times\)**Stack**] & \([\times 1]\) & \([\times 8]\) & \([\times 1]\) & \([\times 8]\) \\ \hline Head & 96.8 & 97.4 & 96.3 & 98.1 \\ Shoulder & 93.8 & 96.0 & 94.1 & 96.2 \\ Elbow & 86.4 & 90.7 & 85.7 & 90.9 \\ Wrist & 80.3 & 86.2 & 80.4 & 87.2 \\ Hip & 87.0 & 89.6 & 85.6 & 89.8 \\ Knee & 80.4 & 86.1 & 80.3 & 87.3 \\ Ankle & 75.7 & 83.2 & 76.0 & 83.5 \\ \# Params & 6M & 25M & 2M & 14.8M \\ [EMAIL_ADDRESS] & 85.5 & 89.8 & 86.1 & 90.8 \\ (Mean) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with state-of-the-art lightweight hourglass methods with the MPII validation dataset.