# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 4 Experiment and Analysis  
The dataset of DukeMTMC-reID [18] which is widely used for the development of person re-identification models, was used for this study. The DukeMTMC-reID [18] dataset composed of the images of 702 persons include 16,522 training images, 17,661 gallery images, and 2228 query images. In addition, the Market-1501 [19] dataset, which is widely used in the field of person re-identification, was used in the experiment to evaluate the performance of the model and increase the evaluation objectivity by examining the dependency of the model on a specific dataset. The Market-1501 [19] dataset, which is composed of images of 750 persons, consists of 12,936 learning images, 13,115 gallery images, and 3368 query images. Prior to the model training, all training images went through the data augmentation process consisting of horizontal flip, random erase, and normalization, and accordingly, 66,088 images for DukeMTMC-reID [18] and 51,744 images for Market-1501 [19] were used during actual training. As the ground truth label for each training image, one-hot encoding vector with corresponding ID value set at 1 for the person on a given image and 0 for all others was used in general. The Adam optimization function [20] was also used during a total of 80 epochs. At this time, the starting training rate was set at 0.001 and was reduced  
Figure 6: Receptive field example of part pyramid on input image. by 0.1-fold for every 30 epochs elapsed. In addition, as the validation data that verified the overfitting status during the training process, randomly selected 6609 images consisting of 10 percent of all training data were utilized. If the calculated validation loss based on the validation data was the smallest, it was saved and used as the weight of the model. Moreover, the zero paddings that filled the remaining part with zero value were set to allow for the inputs during the usage of the kernels by all convolutional layers, and the ReLU [21] function was allowed for all functions to activate the outputs of the layers for the neural network construction. As a method to evaluate the performance of the model, the Rank-1 accuracy which decides on the equivalency based on the query image ID and first rank among the gallery embedding that are aligned based on the cosine similarity was used. Because of the special characteristic of the person re-identification among the intelligent video security schemes that require accuracy in identification such as tracking abnormal behaviors or searching for missing persons, the superior accuracy of the Rank-1 identification result is a critical performance indicator of the proposed method. The first experiment was conducted by switching among ResNet50 [14], VGG 16 [15], and SeResnet101 [22], which were used in by previous person re-identification experiments as the backbone neural network to set the optimal structure of the neural network constituting the re-identification model. In the experimental results shown in (Table 1), ResNet50 [14] showed the best performance among the three backbones. The shallow neural network VGG16 [15] showed difficulty in constructing the feature embedding that sufficiently reflected the visual elements and discriminated person ID with the neural network. On the other hand, the excessively deep neural network SeResNet101 [22] showed decreased performance since unnecessary visual elements as well as the valid visual elements were reflected in the feature embedding. In the second experiment, performance evaluation was conducted by applying different sized pyramids with five configurations composed of the outputs of variously sized receptive fields that included 1*1, 3*3, and 9*9 sized square shapes and 1*3, 3*1, 9*3, and 3*9 sized rectangular shapes on the output feature maps extracted from a backbone as suggested in (Table 2). In (Table 2), the receptive field included in each configuration is marked with \"O\", and the receptive field that is not included is marked with \"X\".