# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.4 Generative Learning for ReID  
One of the main difficulties in ReID tasks is the small diversity of images from the same object with different surroundings and conditions, which poses difficulties for models to generalize well to unseen image contexts. Among the identified datasets, one of their main limitations concerns the uniform illumination, and similar image poses. The use of generative learning, mainly by Generative Adversarial Network (GAN) to increase the amount of training data while presenting the model with more complicated cases, is one common practice in the field of computer vision, and was recently used in ReID tasks. GAN commonly employ the use of two neural networks that compete against each other to become more accurate and output more precise predictions. They are composed of the generator responsible for generating artificial data that can be mapped into the unknown training data distribution. In contrast, a discriminator tries to identify with the generator outputs that correspond to the real examples or the generated ones. The GAN \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & \begin{tabular}{l} **\# Data** \\ **Success** \\ \end{tabular} & **Pros/Cons** \\ \hline \multirow{4}{*}{3D CNN} & [64] & \begin{tabular}{l} 3D CNN, \\ Attention, triple loss \\ \end{tabular} & \begin{tabular}{l} MARS \\ mAP 0.834 \\ \end{tabular} & \begin{tabular}{l} Simple to replicate, \\ reusable \\ state-of-the-art \\ \end{tabular} \\ \cline{2-4}  & [66] & \begin{tabular}{l} 3D- Two stream CNN, \\ Residual attention \\ \end{tabular} & \begin{tabular}{l} MARS \\ mAP 0.740 \\ \end{tabular} & \begin{tabular}{l} Replicable, \\ SOTA \\ \end{tabular} \\ \hline \multirow{4}{*}{RNN} & [6] & \begin{tabular}{l} LSTM, \\ LBP features \\ \end{tabular} & \begin{tabular}{l} iLIDS-VID \\ Acc1 0.493 \\ \end{tabular} & \begin{tabular}{l} Not robust, \\ Old \\ \end{tabular} \\ \cline{2-4}  & [67] & \begin{tabular}{l} LSTM, \\ CNN features \\ \end{tabular} & \
\begin{tabular}{l} MARS \\ Rank1 0.706 \\ \end{tabular} &
\begin{tabular}{l} Simple, \\ replicable \\ \end{tabular} \\ \hline \hline \end{tabular}  
CMC—cumulative matching characteristic (higher, the better), mAP—mean average precision (higher, the better), all measures range: [0.0,1.0].  
\end{table} 
Table 4: Performance evaluation of the reviewed person ReID Sequence methods. Figure 7: Performance summary of the evaluated sequence model methods. training is performed in an adversarial way, improving the capabilities of the generator, simultaneously representing the natural data distribution, and the discriminator to identify the artificially generated images, overcoming the training data limitations. One of the main problems in person ReID concerns the reduced number of images from different poses. To overcome this limitation, a feature distilling generative adversarial network (FD-GAN) is proposed by [68] in combination with a Siamese CNN structure to learn identity-related and pose-unrelated representations. In addition, a novel same-pose loss was also formulated and integrated, requiring the appearance of the same person's generated images to be similar. The proposed FD-GAN explores the Siamese scheme, where an image encoder, an image generator, an identity verification classifier, and two adversarial discriminators are included. The corresponding branch of the network takes a person image and a target pose landmark map as inputs. The image encoder at each branch initially transforms the input person image into feature representations. Then, the identity verification classifier is used to supervise the feature learning for person ReID. The image generator starts by taking the encoded person features and target pose map as inputs, and outputs another image of the same person in a different target pose. The target pose map is represented by an 18-channel map, with each channel representing the location of one pose landmark's location, and with the one-dot landmark location being converted to a Gaussian-like heatmap.