# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.2 Deep Learning Metrics
Considering that each input image contains \(M\) horizontal patches, these image patches are only compared with the corresponding stripe from the other pair images, forming displacement matrices to encode the spatial patterns of each matching patch under the different features representations. The patch matching is then further refined by dividing the patch displacement matrices into \(T\) groups, and within each group, a max out-grouping layer is used. Only prominent feature activations are passed to the next layer, allowing each feature to be represented by multiple redundant channels, enabling the modeling of a mixture of photometric transforms. For body parts, convolution and a max-polling layer are added to the patch displacement matrices to obtain the displacement matrices of body parts on a larger scale. For the final identity recognition, a Softmax function is used to measure the degree of similarity between two input person images, given the global geometric transforms detected on the previous layer. During the model training, several conventional techniques, such as dropout [49], data augmentation, and bootstrap were employed. The model was evaluated using the constructed CUHK03 [46], and the results were collected and compared with other state-of-the-art methods using the CMC metric, with the proposed method achieving a 20.65% when considering Rank-1 rate. The partial region patch makes the method suitable for partial pairs matching, enabling refining similarity metrics, according to the context of the image. In contrast, an unsupervised learning approach is explored by [50], where an unsupervised incremental learning algorithm, denominated TPusion, aided by the transfer learning of pedestrian spatial-temporal patterns from an unlabeled target domain, is used for person ReID. The algorithm transfers the visual classifier trained on a small labeled source dataset to the unlabeled target dataset and learns pedestrian spatial-temporal patterns. A Bayesian fusion model is then used to combine the learned spatial-temporal patterns with extracted visual features to create an improved classifier. A learning-to-rank based on the mutual promotion procedure is used to optimize the classifiers based on the unlabeled data domain incrementally. The proposed framework explores a Siamese Network scheme [8] based on two ResNet50 [14] CNN pre-trained networks to extract visual features from different pair object images. The outputs of the Siamese Network are flattened into two one-dimensional vectors, with the model predicting the identities of each of the input pair images and their similarity score by using cosine similarity. A spatial-temporal pattern learning is formulated considering pedestrian patterns among different cameras and corresponding time intervals of objects that were previously considered similar by the model. In the last stage, the Bayesian fusion model combines the visual features with the spatial-temporal features to achieve a composite similarity score of the given pair of images. Exploring the fact that the Bayesian fusion model is based on the Bayes theorem, it is possible to access the likelihood of the scores of each image that belong to the same object. Model experiments were conducted using the GRID [51], Market-1501 [47], CUHK01 [48], and VIPeR [30] datasets using a cross-validation strategy, where one of the datasets is selected as the source and another one as the target dataset to test, enabling performing cross-dataset person ReID evaluation. The results show that the proposed TFusion model achieved an CMC in Rank-1 of 64.10%, when using the GRID dataset, and 73.13% when using the Market-1501 dataset. The results, when compared to the work of [24] using fusion schemes, are much lower, mostly since the author relies only on ResNet50 as backbones, and uses simple cosine similarity metrics that may not capture other image similarity domains.