# DeepProfile: Accurate Under-the-Clothes Body Profile Estimation
## 5 Experimental Results and Discussions
### Body Profile Results  
For the body profile, we compared the contour line generated by our method with several semantic segmentation methods, including CE2P [1], DeepLabv3+ [7], Gated \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Input Size** & **Orient** & **Params** & \(mAP\) & \(AP^{65}\) & \(AP^{75}\) & \(AP^{85}\) \\ \hline
8-stage Hourglass [33] & \(256\times 256\) & Front & \(98.7M\) & \(78.56\) & \(99.0\) & \(94.4\) & \(67.6\) \\ CPN [36] & \(256\times 256\) & Front & \(178.1M\) & \(77.14\) & \(99.4\) & \(94.2\) & \(63.8\) \\ SimpleBaseline [31] & \(256\times 256\) & Front & \(262.6M\) & \(77.48\) & \(99.4\) & \(94.8\) & \(64.0\) \\ HRNet [38] & \(256\times 256\) & Front & \(243.2M\) & \(77.71\) & \(99.0\) & \(94.2\) & \(62.8\) \\ MSPN [37] & \(256\times 256\) & Front & \(462.1M\) & \(78.64\) & \(99.6\) & \(94.8\) & \(68.2\) \\ DARK(HRNet-W32) [39] & \(256\times 256\) & Front & \(108.9M\) & \(78.89\) & \(99.4\) & \(93.8\) & \(68.8\) \\ DEKR(HRNet-W32) [40] & \(256\times 256\) & Front & \(113.3M\) & \(78.85\) & \(99.2\) & \(94.8\) & \(68.2\) \\ \hline Ours & \(256\times 256\) & Front & \(248.6M\) & \(79.42\) & \(99.8\) & \(95.4\) & \(70.2\) \\ \hline
8-stage Hourglass [33] & \(256\times 256\) & Side & \(98.6M\) & \(69.02\) & \(95.0\) & \(81.0\) & \(43.6\) \\ CPN [36] & \(256\times 256\) & Side & \(177.6M\) & \(66.62\) & \(93.6\) & \(78.2\) & \(35.4\) \\ SimpleBaseline [31] & \(256\times 256\) & Side & \(262.6M\) & \(67.76\) & \(94.6\) & \(78.4\) & \(41.6\) \\ HRNet [38] & \(256\times 256\) & Side & \(243.2M\) & \(68.9\) & \(95.2\) & \(81.4\) & \(44.2\) \\ MSPN [37] & \(256\times 256\) & Side & \(460.5M\) & \(66.72\) & \(94.0\) & \(77.4\) & \(36.8\) \\ DARK(HRNet-W32) [39] & \(256\times 256\) & Side & \(108.9M\) & \(69.16\) & \(94.6\) & \(79.8\) & \(42.4\) \\ DEKR(HRNet-W32) [40] & \(256\times 256\) & Side & \(112.8M\) & \(68.22\) & \(95.6\) & \(79.2\) & \(41.2\) \\ \hline Ours & \(256\times 256\) & Side & \(248.5M\) & \(70.1\) & \(95.4\) & \(84.0\) & \(46.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparisons with other state-of-the-art methods based on our keypoint dataset. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **mAP/Front** & **mAP/Side** \\ \hline Res2 Net-50 [43] & 79.20 & 69.02 \\
1 SMSD w/o dense & 78.78 & 68.56 \\
1 SMSD w/ dense & 79.24 & 69.16 \\
2 SMSDs w/ dense & 79.42 & 70.1 \\
3 SMSDs w/ dense & 79.44 & 70.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Component analysis. “Front” and “Side” denote the front- and side-view images. The accuracy was improved with the increase in the number of SMSD blocks. “1 SMSD w/o dense” means that the dense connections from \(m1\), \(m2\), \(m3\) and \(m4\) to \(p1\) and \(p2\) in the SMSD block were removed. SCNN [10], DenseASPP [9], SPNet [13] and RePRI [12]. In order to obtain a fair result, we labeled our dataset with region segmentation annotation and all these segmentation methods were retrained on this newly created dataset. The comparison results are shown in Figure 7. Most of the methods obtained good visual results if the human subject was dressed in tight-fitting clothing and the background image was simple, such as the results shown in the first and second rows. However, if the human subject was dressed in loose-fitting clothing, the accuracy of the semantic segmentation methods decreased. As shown in the third row, because of the long skirt, the results of CE2P, Deeplabv3+, DenseASPP, Gated-SCNN and SPNet were not satisfactory around the crotch area compared with the ground truth. Furthermore, as shown in the fourth row, due to the loose coat, most of the semantic segmentation methods could not precisely locate some parts (such as the armpit), which affected the precision of the body profile. By contrast, our method was able to obtain more accurate results because the body profile was generated through extracted contour keypoints. Moreover, our method required less data annotation than these semantic segmentation approaches.