# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.1 Feature Learning
The local branch aims to learn the most discriminating local visual features of the surroundings of a people bounding box. In contrast, the second branch is responsible for learning the most discriminating global level features from the entire person's image. The joint learning scheme is employed for concurrently optimizing per-branch discriminative feature representations, and discovering correlated complementary information between local and global features by subjecting both local and global branches to the same identity label supervision. For sharing low-level features, a multi learning methodology is explored, as in the work proposed by [27]. An inter-branch common learning inter-permutation is to be shared on the first convolution layer, with the intuition that lower convolution layers capture low levels features, such as edged and corners that are common patterns to all images, while the complementary discriminative features from local and global representations are learned independently, and related to a given identity label. Moreover, a structure sparsity-induced regularization [28] is introduced to discourage the use of irrelevant features while encouraging discriminative features, to learn concurrently on both local and global contexts, and to maximize a shared identity matching objective. The final global feature representation corresponds to a sparsity measure with LASSO [29]. Cross-entropy is then used as the loss function for both global and local branches, in order to optimize person identity classification and a pairwise person ReID. Concerning the distance metrics for person ReID, a 1024-D deep feature representation is employed using only a generic distance metric without camera-pair specific distance learnable metrics. The models were evaluated on the CUHK01, VIPeR [30], CUHK03, Market-1501, and GRID datasets. On CHUK03, with the proposed method achieving a CMC of 83.2% Rank-1 using labeled objects and 80.6% with automatically detected objects. On Market-1501, the method achieved an CMC Rank-1 of 85.1% on single query, and 89.7% for multi-query. On CHUK01, the method achieved a CMC for Rank-1 of 91.2% when applying an 871/100 dataset split and 76.7% using a 486/485 split. For a GRID dataset, the method obtained a CMC Rank-1 of 37.5%. Finally, as to the VIPeR dataset, the method achieved a CMC Rank-1 of 50.2%. In most datasets, the proposed method surpassed the compared state-of-the-art approaches except for the VIPeR dataset. The combination of local and global features potentiates model generalization, avoiding overfitting to image-specific features. In [31], a novel deep ReID CNN is proposed for omni-scale feature learning (OSNet). The model is based on residual blocks composed of multiple convolutional streams, with each detecting feature at a certain scale. A novel unified aggregation gate is then introduced to dynamically fuse multi-scale features with output-dependent channel-wise weights. To efficiently learn spatial-channel correlations and to avoid over-fitting, pointwise and depth-wise convolutions are used. Depth-wise separable convolutions are also adopted to reduce the number of parameters. Person matching is based on the \(\zeta 2\) distance from 512-D feature vectors extracted from the last layer. Two OSNet models were trained for comparison proposes, with the first model being trained from scratch during 350 epochs using Stochastic Gradient Descent (SGD) as an optimizer. In contrast, a second model was fine-tuned using ImageNet [9] weights and AMSGrad optimizer [32]. The images were resized to \(256\times 128\), and common data augmentation techniques, such as random flip, random crop, and random image patch, and random erase [33], were applied. Model experiments were conducted on six widely used person ReID datasets: Market-1501, CUHK03, DukeMTMC-reid, MSMT17, VIPeR, and GRID datasets.