# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.1 Feature Learning  
Considering the features extracted from images, person ReID methods can explore the extracted feature from a global and local perspective, to better represent the object of interest. Global feature learning usually provides a single feature from the target image, making it difficult to capture detailed information of the person image. To overcome this problem, distinguishable local features are also used to capture subtle invariant features that are often combined in a fusion scheme. An example of the use of a fusion scheme to combine global and local features for person ReID is presented by [19]. It consists of the formulation of an image-to-video person ReID as a classification-based information retrieval problem, where a model of \"person appearance\" is learned from the gallery images, and the identity of the interested person is determined by the probability that the corresponding probe image belongs to one of the gallery images. To learn a model of person appearance, two kinds of features, Kernel Discriminator (KDES) [20] and CNN, are extracted from each person's image. Then, a Support Vector Machine (SVM) model is employed to learn the model. For ReID, three fusion schemes, early fusion, product rule, and query-adaptive late fusions, are proposed to aggregate the features. A ranking scheme in descending order of similarity is employed between the query image and the learned model to determine the most likely image pair. The work was evaluated in two benchmark datasets, CAVIAR4 reid [21] and RAID [22], and by using CMC [23] as evaluation measures for person ReID. The model achieved a CMC of 96.11% when using the CAVIAR4 reid dataset, and contained balanced cases combined with data augmentation during training, while employing late adaptive feature fusion. In contrast, when using the same dataset containing imbalanced cases and the same training and fusion methods, the model obtained an CMC of 93.33%. As for the RAID database, the model presented a CMC of 94.29% when using data augmentation during training and late feature fusion. The same author refined in [24] its previous image-to-video person ReID framework [19] by adding two extra features: the Gaussian of Gaussian (GOG) [25] and learned features from Residual Neural Network (ResNet) [14]. The same feature fusion methodology employed in its previous work [19] was used, and the newly added features were evaluated using the same CAVIAR4 reid and RAID databases. The model on CAVIAR4 reid, containing balanced cases, and using late adaptive fusion combined with data augmentation during training attained an CMC of 86.39%. In contrast, on CAVIAR4 reid and  
Figure 4: Deep learning-based person re-identification methods. with imbalanced cases, as well as the same fusion scheme and augmentation, the model achieved a CMC of 91.94%. Conducted experiments on the RAID database using the same fusion scheme and augmentation techniques achieved a CMC of 92.80%, proving the effectiveness of the newly added features. When compared to the previous work, the performance fell by approximately 2% on both datasets, mainly due to DNN co-adaptation that led to some degree of overfitting. While these works are somehow complementary, the suggested approach can overcome the difficulties in learning cross-scale features by learning multi-scale complementary features. Jointly learning of local and global features using a CNN is proposed by [26], exploring advantages of jointly learning local and global features in a CNN, in order to obtain correlated local and global features in different contexts scenarios for person ReID. A deep two-branch CNN architecture was proposed, with one branch being responsible for learning localized features (local branch) and the second directed to learning global feature (global branch); the two components were not independent, but synergistically correlated and jointly learned, concurrently. The proposed joint learning multi-loss (JLML) CNN model consists of two branch CNN networks.