# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
## 2 Related Works
### Pedestrian Misalignment  
Pedestrian misalignment caused by detectors or pose variations is a main challenge for feature matching across images. Most previous works partition pedestrian bounding box into grids or horizontal stripes to handle misaligned pedestrian images [5; 9; 29; 51]. Nevertheless, these methods only work under the assumption of slight vertical misalignment but not for severe misalignment. Some methods [11; 12] use the pictorial structure to construct well aligned pedestrian images. However, they only use local body parts while ignoring the global context, which results in suboptimal feature learning.  
The recent PIE method [19] proposes a PoseBox fusion (PBF) CNN architecture that takes the original image, the PoseBox, and the pose estimation confidence as input to achieve a globally optimized tradeoff between the global and local feature representations. The PoseBox structure is similar to the pictorial structure [11; 12] in enabling well-aligned pedestrian matching. The PDC method [52] first crops part regions and then transforms each part by a Pose Transformation Network (PTN) to automatically learn transformations such as translation, rotation and scale. The PTN outputs the final transformed part images and hence learns partly aligned representations. These methods all attempt to solve the misalignment problem at image level, with few exceptions that directly handle learned features. For example, [PERSON] et al. [22] followed human body structure to iteratively decompose and fuse features from different semantic region; [PERSON] et al. [53] exploited attention models to implicitly learn effective part representations without guidance of body part locations; and [PERSON] et al. [23] encoded human poses in feature maps through bilinear pooling which aggregates appearance and part maps to compute part-aligned representations. Our method differs from them in the following three aspects.  
* Our work constructs the \"PoseBox\" at feature level instead of the image level. We find that the image level PoseBox would lose their discriminative property due to pose estimation errors. In addition, the affine transformation employed by the PIE method may result in unwanted geometric distortion and deteriorating the intrinsic structure of human body. Figure 2 shows some examples of good and bad PoseBox constructed by PIE. Instead of image level affine transformation, we directly pool local body part features on feature maps, and organize them in a fixed order for feature level alignment (concatenate each body part features along channel dimensions). Meanwhile, we propose to model the spatial dependencies between those local body parts through cross-channel convolution computation. Thanks to the capability of CNN feature maps in context-aware semantic information, we suppose that the feature level alignment would be more robust to pose estimation errors.
* We apply max pooling inside local body part regions so as to find the most salient local details. HFA mechanism and RRW strategy are proposed to make the best of multi-level body part features. Our joint optimization of both global and local features further enhances the discriminative capability of learned feature representations for person re-ID.
* By avoiding complicated affine transformation, we can obtain pose aligned features in a simple and efficient way. Moreover, our method can be easily integrated with different person re-ID networks, and effectively enhance their identification accuracy.