# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 1 Introduction  
Person re-identification refers to identifying a pedestrian of interest based on external information or characteristics of walking from a number of people captured in single or multi-camera environments. It is being regarded as an essential technique for the intelligent video surveillance systems such as tracking offenders or searching for missing persons [1; 2]. Recent person re-identification methods use deep neural network to transform the person images of both a query and a gallery set into feature embedding. Then, the similarity between the feature embedding of the query and the ones in the gallery set is estimated to verify the identity of person of interest by selecting similar images from the gallery set. However, in an actual environment, the identity of a person can be easily confused depending on the differences in the captured time, place, or viewpoint. Because a large difference in appearance of person such as an appearing color, pattern of clothing, belongings, or exposed skin such as face, arms, and legs can be caused by the change of such conditions. Therefore, an intra-class variation that phenomenon of identifying the same person differently or an inter-class variation that phenomenon of identifying the different person as the same can happen easily. Due to such variations, the similarity between the same person can be estimated low, or the similarity between the different person may show high. As a method to solve such variations, recent studies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12] have mainly used the following two types of methods. In the first method, a neural network uses additional meta-information such as time, place, and viewpoint at the time of photographing of the pedestrian with a person image [3; 4]. Although this method has the advantage of improving the person re-identification accuracy, it is difficult to obtain such meta-informationautomatically from an actual environment. Moreover, additional considerations and resources are required to process acquired information into a data form that can be input into a neural network. Therefore, the second method, which only uses images as input data, has been mainly studied. To obtain the performance close to the first method without additional meta-information, this method utilizes various preprocessing techniques or utilizes a number of auxiliary neural networks or special modules to extract more discernable representative features from the input image while constructing a neural network [5; 6; 7; 8; 9; 10; 11; 12]. Although this method has an advantage of achieving performance close to the first method, the model can become excessively complicated due to the requirement of several auxiliary neural networks within the neural network or an algorithm in addition to the neural network [5; 6; 9]. Due to the limitations of the image segmentation method or filter shape used by the existing method, it is difficult to reflect the visual elements of various sizes and shapes appearing in the person image into the feature embedding [5; 6; 7; 8; 9; 10; 11; 12]. Therefore, in order to enable robust re-identification against the intra/inter-class variations using only the given person images without any other meta-information, a new person re-identification method with dual pyramids that extracts various visual feature elements scattered in various areas of a person image and reflects them in the feature embedding is proposed in this paper. In dual pyramids, a scale pyramid reflects the visual feature elements in various sizes and shapes, and then a part pyramid selects visual feature elements and differently combines them for the feature embedding per each region of the person image. For such purpose, a scale pyramid has different sized kernels that are arranged in serial and parallel fashions, and a part pyramid uses divided and combined feature maps extracted from the scale pyramid to compose feature embedding.