# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.1 Feature Learning
Collected results showed that the model achieved overall supremacy when compared with most of the state-of-art methods, attaining a CMC Rank-1 of 94.8% on Market-1501, 72.3% on CUHK03, 88.6% on Duke, and 78.7% on MSMT17. The proposed fusion scheme shows the effectiveness of the omni-scale features in different scales to comply with a large range of possible viewpoints from image pairs. To improve the capabilities of attention mechanisms and obtain fine detailed features for person ReID, ref [34] proposes a feature refinement process in combination with filter network, by weakening the high response features and eliminating the interference raised by the background information. The model includes a network formed by the weaken feature convolution blocks based on ResNet, in combination with a multi-branch scheme. An attention mechanism is also set in place to act as an attention feature map in the convolution module, with higher values corresponding to regions where the model has paid more attention. Extensive experiments were conducted in the Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 person ReID benchmarks datasets, with the proposed model achieving a mAP of 94.2% in Market-1501. An automatic search for a CNN architecture, specifically suited for the ReID problematic was proposed by [35], and denoted as Auto-ID. The method is based on the neural architecture search (NAS) [36] to automate the process of architecture design without human effort, directed to the task of ReID, by using a retrieval search-based algorithm. This design enables to obtain more optimal architectures that make the best use of human body structure information for person ReID, eliminating human expert efforts in the manual design of CNN models for the task. The model starts by integrating structural body cues into the input tensors, and then by vertically splitting the input feature tensor into four body part features, averaging each tensor part into a vector, and transforming each of the tensors into a new part feature vector using a linear layer. The obtained part vectors interact between them via a self-attention mechanism, enabling each part of the vectors to incorporate more specific body part information. Each obtained part vector is repeated and concatenated to recover the original spatial shape as the input tensor. Finally, the formed global feature tensor is fused with the original input tensor, using a one-by-one convolutional layer. A class-balance data sampler to equal the sample batch data for the triplet loss is used to overcome the original sensitivity of triplet loss to the batch data. This sampler first samples some identities, uniformly, and then, for each identity, it randomly samples the same number of images. To better explore the benefits from the cross-entropy and triplet losses, a mixture retrieval loss between sample loss and triplet loss is considered. Experiments were conducted on the Market-1501, CUHK03, and MSMT17 public databases, with the proposed Auto-ID model achieving a CMC rank-1 of 95.4% on Market-1501, when using the re-ranking technique, 77.9% on CUHK03 with labeled examples, and 73.3% on detected ones. On MSMT17, the model achieved a CMC Rank-1 of 78.2%. The proposal work enables increasing the ReID performance by taking into consideration attention mechanisms combined with triplet loss methods. A dropout technique was proposed by [4] for learning deep feature representations from multiple domains with CNN. Multi-domain learning is frequently directed toward solving the problem of using datasets across different domains simultaneously, by using all data they provide for the task of multi-domain learning, while robustly handling data domain discrepancies. The central problem in multiple learning relies on the fact that samples from the same domain follow the same underlying data distribution, which degrades the model performance since some neurons may gain focus on some domain representation, while discarding the remainder domains, and leading to a bad model generalization.