# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
## 4 Experiments
### Performance Evaluation
#### 4.3.4 Further Analysis and Discussion  
* **Body part segmentation** To evaluate the impact of body part segmentation errors on our method, we randomly disturbed the position of each body part during training. Here, we adopted two settings: small disturbance (Disturb-small) and violent disturbance (Disturb-violent). We translated the coordinates of each body part up to 6% of input image size for small disturbance and 30% for violent disturbance. Tables 6 and 7 show the results of DJL-S on Market-1501 and CUHK03, respectively. Generally, accuracy changed a little under slight disturbances (from 67.64% to 68.82% for AlexNet on Market-1501) while varied dramatically under large disturbances (still better than base networks). This demonstrates that our proposed method can effectively cope with human body misalignment. * **Low resolution** We evaluated the impact of image resolution on our method. Experiments were conducted on CUHK03. We down-sampled all images in CUHK03 to half of their original size and used those low resolution images for training and testing. The results in Table 7 show that low image resolution degrades the performance of DJL-S.
* **RoI pooling effects at different layers** An important part of our method is to apply the RoI pooling operation to different middle layers. In Tables 8 and 9, we systematically explore the identification performance of different middle convolution by performing RoI pooling on each of them. We experimented with various network structures (AlexNet, Residual-50 and InceptionNet) and found that pooling at relative deeper layer obtains better performance improvements over the base networks. This observation shows that deeper, semantic CNN features contribute more to person re-ID task. * **Hierarchical Feature Aggregation and Region Re-Weighting** We evaluated the effects of Hierarchical feature aggregation and Region Re-Weighting using the base Inception network with different variants of DJL: DJL-S, DJL-S + RRW, DJL-HFA(w/o RRW), and DJL-HFA. DJL-S denotes pooling body part features from a single convolution layer (Inception_3a). DJL-S + RRW further combines RRW strategy with DJL-S. DJL-HFA (w/o RRW) means DJL-HFA without applying RRW strategy, and DJL-HFA is the full version of our proposed method. As depicted in Tables 10 and 11, the DJL-S + RRW achieves performance gain in Rank-1 accuracy compared with the DJL-S network on both Market-1501 and CUHK03 datasets. When adopting DJL-HFA(w/o RRW), the Rank-1 accuracy improved on CUHK03 dataset while dropped slightly on Market-1501 dataset. We believe the performance drop is due to the inconsistent scale and norm of multiple layers (the \"larger\" features would dominate the \"smaller\" ones) [28]. As Region Re-Weighting would automatically learn the scale of features during training procedure, we speculate that integrating RRW with HFA would achieve more performance gain in Rank-1 accuracy. The results in Tables 10 and 11 also demonstrate this: the Rank-1 accuracy arrived at 85.99/85.90 on Market-1501/CUHK03 when using DJL-HFA. Furthermore, we give some illustrations about the learned weight parameters in Table 12, which show the scale and importance differences across multiple layers regions. * **Complementary effects** We evaluated the effects of individual local feature (FC_local), global feature (FC7) as well as their combination on Market-1501 and CUHK03. The results on the two databases are shown in Figure 7. These results demonstrate that, although global and local feature representations alone are competitive for re-ID, further performance gain can be obtained by combining them using our proposed method. This proves that our proposed method can effectively explore the complementary discriminative information in global and local features for more accurate person re-ID.