# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 3 Method  
### Overall Procedure of the Proposed Person Re-Identification Scheme Using Dual Pyramids  
In this section, we introduce an overall procedure of the proposed person re-identification scheme using multiscale feature embedding made by dual pyramids, a scale pyramid and a part pyramid. As shown in the training process of (Figure 1), the proposed scheme uses only the person's image as an input without additional meta-information such as the captured time, location, and viewpoint. The input person image is converted into 15 part-level local feature embedding, and each embedding is used to train by the classification method that infers the person's ID with given cross entropy loss function as the ground truth (GT). The details of embedding, such as creation process and network architecture of sub-module are explained in the following Sections 3.2 and 3.3 with concrete examples. In the subsequent inference process, in addition to the query image that requires re-identification, the gallery images to be used to verify the identity of the query image are given, and the pre-trained re-identification model is used to convert the query image and gallery images into the part-level feature embedding. Fifteen part-level feature embedding created from each image are connected to a single feature embedding, which designates multiscale features extracted from various locations of the input image and then uses them for similarity comparison. The multiscale feature embedding created from the query image is called query embedding, and that from the gallery images is called gallery embedding. The similarity comparison between feature embedding is made based on the cosine similarity, and the set of gallery embedding is sorted based on the similarity with the query embedding. In turn, the identity of the gallery image with the highest similarity is specified as the identity of the query image. The structure of the neural network constituting the person re-identification model is configured in the order of the input, backbone network, scale pyramid, part pyramid, and output embedding module as shown in (Figure 2). First, as for the input image of the neural network, the rectangular images of 1:3 ratio with width of 128 and height of 384 are accepted instead of the square shapes commonly used by the image handling neural networks to reflect general body characteristic of longer height to width of the shoulder. The input image is transferred to the backbone and the subsequent convolutional layer to be converted into a feature map sized (8, 24, 512). Following the convolutional layer, batch normalization (BN) [16] and dropout [17] are placed to prevent overfitting by the previous layers. At this point, if the entire network of ResNet50 [14] is used as a backbone, the size of output is reduced to \(1/32\) of the input, which in turn limits the available features of the subsequent blocks. Therefore, the last convolution block in ResNet50 [14] is excised to produce a lower reduction ratio of \(1/16\) for more feature utilization by the subsequent layers. The extracted feature map from the backbone and subsequent convolution layer is delivered to the scale pyramid to produce six outputs, each of which is (8, 24, 512) size and represents the multiscale features from large to small and both square and rectangular-shaped visual elements of the input image. And these six output feature maps from the scale pyramid are transferred to the input of the part pyramid to generate 15 part-level local feature vectors sized (\(1,1,512\)*6) that originate from various regions of the input with different configuration as shown in (Figure 2). Finally, the vectors are converted to the local feature embedding, with a length of (512), by passing through a convolution layer with the last kernel sized 1*1, batch normalization (BN) layer, and dropout layer as shown in (Figure 2).