# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
## 4 Experiments
### Performance Evaluation
#### 4.3.4 Further Analysis and Discussion
Two example results are shown in Figure 8. As can be seen, even when the probe and gallery pedestrian images have obviously different poses (i.e., they are not well aligned), our proposed method can still correctly retrieve the corresponding gallery images among the first ten ranks. Figure 8: Example person re-ID results by using the base Residual-50 network (Res-50) and the proposed DJL network on Market-1501 database. Correct retrievals are surrounded with green bounding boxes while wrong retrievals are surrounded with red bounding boxes. Figure 7: CMC curves on Market-1501 and CUHK03 when using local, global and hybrid features (global+local) extracted by DJL-S (based on InceptionNet). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Base Network**} & \multirow{2}{*}{**Setting**} & \multicolumn{5}{c}{**Market-1501**} \\ \cline{3-7}  & & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** & **mAP** \\ \hline \multirow{4}{*}{AlexNet} & Base & 57.75 & 77.52 & 84.47 & 89.46 & 33.80 \\  & Proposed & 67.64 & 84.80 & **89.88** & **93.53** & 43.60 \\  & Disturb-small & **68.82** & **84.95** & 89.31 & 93.50 & **44.89** \\  & Disturb-violent & 64.79 & 82.21 & 88.15 & 92.22 & 40.84 \\ \hline \multirow{4}{*}{Residual-50} & Base & 72.42 & 86.49 & 91.03 & 94.42 & 48.01 \\  & Proposed & **78.86** & **90.38** & **93.91** & **96.35** & **55.49** \\ \cline{1-1}  & Disturb-small & 77.76 & 89.88 & 92.96 & 96.02 & 54.62 \\ \cline{1-1}  & Disturb-violent & 75.95 & 88.60 & 92.37 & 95.19 & 52.71 \\ \hline \multirow{4}{*}{InceptionNet} & Base & 79.66 & 91.51 & 94.54 & 96.50 & 56.59 \\  & Proposed & **85.12** & **93.91** & 95.69 & 97.51 & 64.82 \\ \cline{1-1}  & Disturb-small & 84.53 & 93.79 & 95.93 & 97.54 & **64.89** \\ \cline{1-1}  & Disturb-violent & 83.61 & 93.65 & **95.99** & **97.60** & 63.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Identification accuracy (%) and mAP (%) of the proposed method with different base networks on Market-1501 when different disturbances are applied to the segmented body parts. The best results under different settings are marked in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base Network**} & \multirow{2}{*}{**Setting**} & \multicolumn{5}{c}{**CUHK03**} \\ \cline{3-5}  & & **Rank-1** & **Rank-5** & **Rank-10** & **Rank-20** \\ \hline \multirow{4}{*}{AlexNet} & Base & 53.03 & 79.53 & 87.82 & 94.21 \\  & proposed & **71.95** & **90.30** & **94.91** & **98.16** \\  & Disturb-small & 68.31 & 89.19 & 93.86 & 97.07 \\  & Disturb-violent & 62.07 & 84.84 & 91.49 & 96.03 \\  & Low-resolution & 60.35 & 83.71 & 90.59 & 95.49 \\ \hline \multirow{4}{*}{Residual-50} & Base & 61.79 & 85.46 & 92.31 & 97.86 \\  & proposed & **80.83** & 95.92 & 98.66 & 99.54 \\ \cline{1-1}  & Disturb-small & 80.53 & **96.45** & **99.01** & **99.71** \\ \cline{1-1}  & Disturb-violent & 73.40 & 93.23 & 96.75 & 99.25 \\ \cline{1-1}  & Low-resolution & 75.58 & 93.68 & 97.28 & 9 9.15 \\ \hline \multirow{4}{*}{InceptionNet} & Base & 80.85 & 95.90 & 98.17 & 99.48 \\ \cline{1-1}  & proposed & **84.25** & 97.40 & **98.86** & 99.67 \\ \cline{1-1}  & Disturb-small & 83.38 & **97.49** & 98.81 & 99.52 \\ \cline{1-1}  & Disturb-violent & 82.41 & 97.42 & 98.84 & **99.69** \\ \cline{1-1}  & Low-resolution & 82.80 & 97.12 & 98.64 & 99.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Identification accuracy (%) of the proposed method with different base networks on CUHK03 when different disturbances were applied to the segmented body parts and when low resolution images were used. The best results under different settings are marked in bold.