# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 2 Deep Learning for Object Re-Identification  
A turning point in the history of machine learning and computer vision was reached by researchers of the University of Toronto, who proposed a new image classification approach and achieved excellent results in the ImageNet [9] competition [10]. The winning proposal, defined as AlexNet, consisted of a CNN composed of a set of stacked deep layers and dense layers that enabled the reduction of the error drastically. However, the first appearances of CNN dated from 1990, when [PERSON] et al. [11] proposed a CNN method addressing the task of hand-written digit recognition to alleviate the work of the postal office.  
A CNN multi-layer contains at least one layer to perform convolution operations from image inputs, by using filters of kernels that are translated across and down the input matrix, to generate a feature representation map of the original image input. The characteristics of these filters can be widely different, and each one of them is composed of learnable parameters, updated through a gradient descent optimization scheme. The same layer can employ other filters. For the same part of the image, each filter produces a set of local responses, enabling correlation of specific pixel information with the content of the adjacent pixels. After the proposal by [10], many different network architectures were developed to address such a problem, each one with its inner characteristics, to name a few, the VGG [12], Inception [13], and ResNet [13] architectures. Many other network architectures were proposed, but in summary, they all share some building blocks.  
In regard to the ReID task, the most used backbone model architecture is ResNet [14], due to its flexibility and ease of reusing and implementation to solve new problems. Most of the ReID tasks explore the use of pre-trained ResNet as backbones for feature extraction for the object ReID task.  
ResNets can address the vanishing gradient problem when the networks are too deep, making the gradients quickly shrink to zero after several chain rule applications, leading toward \"not updating\" the weights and, therefore, harming of the learning process. With  
Figure 2: Common base architecture for person ReID.  
ResNets, the gradients can flow directly through the skip connections backward from later layers to the initial filters.  
ResNets can have different sizes, depending on how big each model layer is, and how many layers it contains. As an example, ResNet 34 [15] contains one convolution and pooling layer step, followed by four similar layers (Figure 3). Each layer follows the same pattern by performing \(3\times 3\) convolutions with a fixed feature map dimension, and by bypassing the input every two convolutions. A concern with the special characteristics of ResNet 34 is the fact that the width \(W\) and height \(H\) dimensions remain constant during the entire single layer. The size reduction is achieved by the stride size used in the convolution kernels, instead of the polling layers commonly used in other models.  
Every layer of a ResNet is composed of several blocks, enabling it to go deeper. This deepness is achieved by increasing the number of operations within a block, while maintaining the total number of layers. Each operation comprises a convolution step, batch normalization, and a ReLU activation to a particular input; except for the last operation of the block, which does not contain a ReLU activation function.  
The aforementioned characteristics make ResNets particularly suitable for object ReID, since it enables shallow lower-level features to be reused at higher-level stages. This scheme allows exploring relevant information for ReID task from all layer feature maps, instead of relying only on more abstract summarized features provided by the higher layers.