# 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
The encoding is performed by using a 5-block convolution-BatchNorm Normalization (BN)-ReLU subnetwork, generating a 128-dimensional pose feature vector. The visual features, target pose features, and an additional 256-dimensional noise vector, sampled from standard Gaussian distribution, are then concatenated and input into a series of 5 convolution-BN-dropout-ReLU upsampling blocks to output the generated person images. Concerning training, it was performed in three main stages. Initially, the Siamese network baseline built on ResNet-50, using the weights from ImageNet [9], was established. The network was firstly optimized with SGD and trained during 80 epochs. In the second training stage, the encoder and validation classifier were fixed, and the generator was integrated. Adam optimizer [69] was employed to optimize the generator, while the identity discriminator and posed discriminator were optimized with SGD. Lastly, a global fine-tuning was done on the model through all blocks in an end-to-end fashion. For performance evaluation, Market-1501, CUHK03, and DukeMTMC-ReID datasets were used, with mAP and CMC Rank-1 accuracy metrics being adopted for performance evaluation on all the three datasets, and the proposed FD-GAN obtained a CMC of 90.5% on Market-1501, 92.6% on CUHK03, and 80.0% on DukeMTMC-ReID. The inclusion of the FD-GAN and pose encoder enables a substantial increase in model performance. Another standard limitation concerns the lack of diversity in the image domain, namely, different images gathered and subjected to other illumination conditions. A style transfer GAN is proposed by [70] to serve as a data argumentation approach to smooth camera style disparities. The method employs CycleGAN [71] to style transfer trained labeled images from different cameras aiming an increase of the diversity of training examples, avoiding model overfitting. Focusing on a better handling of noise, a smooth label regularization is introduced. The style transfer method is evaluated on the ReID task using the Market-1501 and DukeMTMC-ReID datasets, with the proposed model achieving a mAP of 71.55% in the Market-1501. The vast domain range of images poses difficulties to the ReID tasks. To reduce the impact of image domain diversity, ref [72] proposes a joint learning scheme to improve domain adaptation, to disentangle ID-related/unrelated features, which enforces adaptation to focus on the ID-related features space only. The disentangle module is responsible for encoding cross-domain images into a shared appearance and two separated structure spaces, with the adversarial alignment being performed by the adaptation module. Extensive experiments using the Market-1501 and DukeMTMC-ReID datasets were performed, with the model achieving a Rank-1 of 83.1 in the Market-1501. A careful evaluation of methodologies for person ReID was performed by [38]. The evaluation starts by setting a baseline backbone architecture based on ResNet-50 [14] with weights initialized on ImageNet [9], and changing the dimension of the fully connected layer to the number of identities in the training set. Similar assumptions were made for all experiments for training as described in the article [38]. Several training tricks were evaluated, such as warm-up learning rate [73], to bootstrap the network for better performance; random erasing augmentation [33], where an image \(I\) in a mini-batch has the probability of undergoing random erasing of \(p_{e}\); label smoothing [44] to prevent the model from overfitting the training ID, where a small constant \(e\) is introduced to avoid the overfit of the training set; last stride [74] to obtain a higher spatial resolution, enriching the granularity of features.