# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
## 4 Experiments
### Performance Evaluation
#### 4.3.4 Further Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Market-1501**} \\ \cline{2-6}  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** \\ \hline DJL-S & 85.12 & 93.91 & 95.69 & 97.51 & 64.82 \\ DJL-S + RRW & 85.21 & 93.74 & 95.78 & 97.60 & 65.38 \\ DJL-HFA (**w/o RRW**) & 84.95 & 94.15 & 96.38 & 97.74 & 65.29 \\ DJL-HFA & **85.99** & 94.15 & 96.29 & 97.77 & 65.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Effects of Region Re-Weighting and Hierarchical Feature Aggregation using the base Inception network on CUHK03. Identification accuracy (%) is reported. The best Rank-1 result is marked in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base Network**} & \multirow{2}{*}{**Pooling Layer**} & \multicolumn{5}{c}{**Market-1501**} \\ \cline{3-6}  & & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** \\ \hline \multirow{3}{*}{AlexNet} & Conv3 & 66.30 & 84.29 & 89.58 & 93.23 & 42.75 \\  & Conv4 & 67.64 & 84.80 & 89.88 & 93.53 & 43.60 \\  & Conv5 & **69.83** & **85.66** & **90.65** & **94.00** & **45.17** \\ \hline \multirow{3}{*}{Residual-50} & Res3a & 77.02 & 89.88 & 93.29 & 95.87 & 54.17 \\  & Res4a & 78.86 & 90.38 & 93.91 & **96.35** & 55.49 \\  & Res5a & **79.48** & **91.30** & **94.51** & 96.20 & **57.53** \\ \hline \multirow{3}{*}{InceptionNet} & Inception\_1a & 82.90 & 92.99 & 95.16 & 97.00 & 61.24 \\  & Inception\_2a & 84.59 & **94.83** & **96.53** & **98.19** & **65.89** \\ \cline{1-1}  & Inception\_3a & **85.12** & 93.91 & 95.69 & 97.51 & 64.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Identification accuracy (%) and mAP (%) of the proposed method with performing RoI pooling at different middle layers on Market-1501. The best result over various pooling layers is marked in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**CUHK03**} \\ \cline{2-7}  & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline DJL-S & 84.25 & 97.40 & 98.86 & 99.67 \\ DJL-S + RRW & 84.29 & 97.15 & 98.80 & 99.67 \\ DJL-HFA (**w/o RRW**) & 84.72 & 97.17 & 98.40 & 99.34 \\ DJL-HFA & **85.90** & 97.79 & 98.90 & 99.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Effects of Region Re-Weighting and Hierarchical Feature Aggregation using the base Inception network on Market-1501. Identification accuracy (%) and mAP (%) are reported. The best Rank-1 result is marked in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base Network**} & \multirow{2}{*}{**Pooling Layer**} & \multicolumn{5}{c}{**CUHK03**} \\ \cline{3-6}  & & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline \multirow{3}{*}{AlexNet} & Conv3 & 68.13 & 89.12 & 94.84 & 97.94 \\  & Conv4 & 71.95 & 90.30 & 94.91 & 98.16 \\  & Conv5 & **74.22** & **91.25** & **95.37** & **98.64** \\ \hline \multirow{3}{*}{Residual-50} & Res3a & 77.40 & 94.63 & 98.44 & 99.57 \\  & Res4a & 80.83 & 95.92 & 98.66 & 99.54 \\  & Res5a & **83.97** & **96.97** & **98.67** & **99.61** \\ \hline \multirow{3}{*}{InceptionNet} & Inception\_1a & 82.66 & 96.66 & 98.42 & 99.33 \\  & Inception\_2a & 83.01 & 96.98 & 98.75 & 99.53 \\ \cline{1-1}  & Inception\_3a & **84.25** & **97.40** & **98.86** & **99.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Identification accuracy (%) of the proposed method with performing RoI pooling at different middle layers on CUHK03. The best result over various pooling layers is marked in bold.