# Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
## 3 Results
### Complexity Analysis  
Table 2 compares the average precision between Openpose models. The experimental setup involved systematically substituting the backbone one at a time and conducting comparisons to identify the most optimal model, striking a balance between accuracy and computational efficiency. The first \(12\) layers of MobileNet-v1 are chosen for the backbone. The total number of stages of OpenPose is six, and experiments [13,18] have shown that the accuracy does not increase significantly from stage \(2\) to stage \(6\). This is the basis for us to be able to omit stages that are homogeneous and obtain the final network with a structure of only two stages. These two stages have better performances than the six stages of the OpenPose network because of their deeper design. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **AP, \%** & **GFLOPs** \\ \hline First \(6\) layers (v1) & \(37.6\) & \(23.3\) \\ \hline Dilated first \(12\) layers (v1) & \(43.5\) & \(27.7\) \\ \hline Dilated first \(13\) layers (v1) & \(44.7\) & \(31.3\) \\ \hline First \(6\) bottle-neck layers (v2) & \(39.5\) & \(27.2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Component selection from the MobileNet series for feature extractor (model’s build with \(2\) branch architecture of Openpose CMU). The OpenPose experiment was set up as a reference for block-to-block comparisons in terms of accuracy/complexity. The input resolution of the network is 368 \(\times\) 68, which is the same as the input of the first 10 layers of VGG-19. The results suggest that the precision of stages 2-6 are homogenized and have slight increases in accuracy (Table 3). Thus, in terms of insight, we experienced that it is possible to remove a few of the homogenous stages and increase the depth of the remaining homogenous stages for the same accuracy. In our proposal, the complexity of the algorithm and the number of parameters of the proposed algorithm have been reduced, as shown in Table 4. For the proposed method, the feature extractor (Backbone + Cpm block) is reduced to 4.28 GFLOPs, equivalent to 10.5%, reduced to 58.2%, with Stage 1 and each homogenous stage block reduced to 18.3%. The above comparisons are compared with experiments on the OpenPose-2016 [18] method. The proposed method exhibits a computational complexity of 8.97 GFLOPs and encompasses approximately 4.09 million parameters. These attributes empower the method with the capability to execute on edge devices in real-time. The performed comparisons of the proposed method are implemented on the COCO dataset to evaluate the operation of the method on the adult dataset. The comparison between SOTAs includes both top-down (object detection) and bottom-up (non-object detection: OpenPose-2016, OpenPose-2018, and proposed method) approaches. **Pose estimation on COCO. ** In addition, using the COCO dataset for comparison is objective because some methods have not been trained on the infant dataset and have low performance on that dataset (Table 5). The proposed method achieves 64.4 AP on COCO with an input size of 368 \(\times\) 368, which is the method with the best performance among comparing methods with 8.9 GFLOPs. However, the accuracy of the proposed method is similar to top SOTA methods such as HRNet-W48 [27], Transpose [28], HRFormer-B [29], TokenPose-L/D24 [30], OpenPose-2018 [13]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**GFLOPs**} & \multicolumn{2}{c}{**Params (M)**} \\ \hline  & **Block** & **Total** & **Block** & **Total** \\ \hline
**Backbone** & 3.72 & 3.72 & 1.61 & 1.61 \\ \hline
**Cpm block** & 0.56 & 4.28 & 0.27 & 1.88 \\ \hline
**Stage1** & 1.28 & 5.56 & 0.6 & 2.48 \\ \hline
**Homogenous Stage 2** & 3.41 & **8.97** & 1.61 & **4.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Complexity and number of parameters (proposed model).