# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 2 Related Work
Therefore, recent studies [10, 11, 12] simplified the meaning of a local area as a patch of a horizontally divided person image. The feature map extracted from the backbone such as ResNet50 [14] or VGG16 [15] is simply divided horizontally, and each region becomes an origin area of each local feature embedding. Accordingly, previous study [10] divides the feature map from ResNet50 [14] backbone into six regions horizontally and construct local feature embeddings representing each region by global average pooling. When each feature embedding is configured on the divided regions with fixed size, the visual feature elements that exist over two or more neighboring regions that are larger than the fixed size are difficult to be reflected in the feature embedding. Therefore, other studies [11,12] tried to solve this problem using a so-called feature pyramid. In [11,12], a feature pyramid was configured by combining six horizontally divided basic regions first, and then overlapping and tying between the successive neighboring regions, with a total of one to six in each group, creating a total of 21 combined regions. The study [12] also used a similar feature pyramid, which is configured by combining horizontally divided eight basic regions first, and then tying between the adjacent neighboring regions without overlapping, with a total of eight, four, two, and one in each group to create a total of 15 combined regions. These combined regions are converted into local feature embedding through the adding up of vectors created by global average pooling and global max pooling. However, in the case of constructing a pyramid with multiple combination of regions that overlap each other, each basic region is used to configure multiple combination of the regions. In particular, the basic region located in the center is used more often than the basic regions outside the center region. For this reason, regardless of the actual importance of each basic region, more regions located in the center of the image are more often unconditionally utilized to configure the combination of regions. This is a problem since the crucial visual feature elements such as shoes, hair color, and exposed facial features are difficult to use as important sources. In addition, when the sum of the maximum pooling and the average pooling is used for the construction of the feature embedding, the value in the embedding eventually becomes an ambiguous value rather than an average or maximum, which may result in a deterioration in the discrimination of embedding. Therefore, this study proposes a person re-identification method that applies a scale pyramid module that allows the neural network to extract visual feature elements that appear in various sizes and shapes in the input person image and a part pyramid module that allows configuration of more accurate feature embedding by appropriately reflecting extracting features according to each region. The scale pyramid module used in the proposed method is designed by connecting the convolutional layers with kernels of different sizes and shapes in series and a parallel manner, so the receptive fields of various sizes including square and rectangular shapes can be acquired from the input image. In addition, to construct discriminative feature embedding that evenly reflects visual feature elements that exist across either narrow or wide areas within the input image, a part pyramid module is horizontally divided into eight segments and then serially connected by regions, a total of eight, four, two, and one in each group, without a mutual overlap to produce a total of 15 regions to configure feature embedding in each region. Each region is converted into local feature embedding by using only global average pooling instead of the complex summation of maximum pooling and average pooling. This proposed method can form the feature embedding that independently reflects the visual feature elements of various sizes and shapes that exist on each region of the input person image.