# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.1 Feature Learning
To overcome this problem, a domain guided dropout algorithm was proposed to avoid the model to learn only from domains where samples follow the same underlying data distribution. The CNN models are trained from scratch using all data domains using a single Softmax loss, creating a solid baseline. For each domain, a forward pass was performed on all data domains, and the impact that each neuron had on the objective function was quantified. After a few epochs of training, the standard network dropout layer was replaced by the proposed domain guided dropout layer, and the training process continued for several epochs to guide the neurons to the effective domain, enabling the CNN to learn more discriminative features for all of them. Experiments were conducted using the CUHK03, CUHK01, CUHK03, and PRID datasets, and compared with state-of-the-art methods using the CMC metrics. The proposed method outperformed the studied state-of-the-art methods, achieving a CMC Rank-1 of 75.3% on CUHK03, 66.6% on CUHK01, and 64.0% on PRID. The introduced domain dropout layer acts as regularized mechanism, avoiding the neurons co-adaptation to specific domains, reducing overfitting that degraded the ReID performance. Attention mechanism has gained relevance in recent years, showing good performance in many fields, and it is often used as a local feature learning mechanism, which is useful for the task of ReID. One example of an attention mechanism for deep learning networks was presented by [37] to provide simultaneously masks-free and foreground-focused samples for the inference phase. The main objective was to generate synthetic data that are composed of interleaved segments gathered from the original learning image set, while using class information only from specific segments. The proposed augmentation technique was evaluated using a baseline method proposed by [38], based on a deep learning-based classification framework using the ResNet-50 as a feature extractor, with weights initialized on ImageNet [9], along with a bag of tricks, known to be particularly effective for person ReID tasks. Since the richly annotated pedestrian (RAP) [17] dataset does not provide human body segmentation annotations, human binary segmentation masks were extracted using Mask-RCNN [39] to obtain the human body segmentation binary masks. Afterwards, fake images were generated to enlarge the dataset. For a matter of performance evaluation of the augmentation method, the default parameter settings detailed on the official project and the same weights were reused without modifications. The models were evaluated on two different loss schemes: Softmax and Triplet, with the results being slightly better when using triple loss on the RAP dataset. Accurately, the model presented a mAP Rank-1 of 62.9% when considering the upper body part, and 65.7% for the full body. The proposed augmentation technique enabled an increase in the performance of the baseline method by almost 20%. Another attention mechanism based in Deep Learning (DL) models was proposed by [40] to overcome the problem of learning fine-grained pedestrian features that are useful for pedestrians ReID. A self-denominated HydraPlus-Net (HP-net), which multi-directionally feeds the multi-level attention maps to different feature maps and to different feature layers, is used. The method enables the model to capture multiple attention from a low-level to a semantic level by exploring the multi-scale selectiveness of attentive features, to enrich the final feature representations for the pedestrian image. The proposed approach was evaluated on three publicly standard datasets: CUHK03, VIPeR, and Market-1501 datasets. The model achieved a CMC Top-1 of 91.8 % on CUHK03, 56.6% on ViPer, and 76.9% on Market-1501. The proposed method achieved top performance results, proving the effectiveness of attention mechanisms to capture relevant features maps for ReID tasks.