# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 6 ReID and Spatial-Temporal Multi Object ReID Methods
### Spatial-Temporal Constrained and Multi Object RelD Methods
#### 6.2.1 Deep Learning Metrics for Vehicle ReID  
Similar to person ReID, triplet loss are a excellent tool for vehicle ReID tasks. A variation of the triplet loss commonly used in deep learning methods, defined as a group-sensitive-triplet embedding (GS-TRE), was proposed in [82] to recognize and retrieve vehicles, where the intraclass variance is elegantly modeled by incorporating an intermediate representation \"group\" between samples and each vehicle in the triplet network learning. The main objective is to address the car ReID problematic, namely the fact that common deep metric learning with a triplet network common configuration ignores the impact of intra-class variance-incorporated embedding on the performance of vehicle ReID, where robust fine-grained features for large-scale vehicle ReID have not been fully studied. In addition, a clustering strategy to derive group labels, and in particular, an online clustering method, is employed, and a mean-valued triplet loss [83] is also proposed to enhance the learning of discriminative features. The performance of the proposed group-sensitive-triplet embedding (GS-TRE) was evaluated on the VeRi-776 and VehicleID datasets, with the model obtaining a mAP of 0.743 on the VehicleID dataset, with the modified triplet loss well suitable to help in the ReID task. An improvement of triplet loss is presented by [84], focusing on two aspects: first, a stronger constraint, namely classification-oriented loss augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is proposed in combination with a classification-oriented loss to implicitly impose a constraint for the embedded features of the images of the same vehicle to be similar, and also by ensuring negative samples in one triplet act as positive samples in another triplet. The system architecture consists of three parts: a shared deep CNN to learn a mapping from raw images to Euclidean space, with the distance reflecting the relevance between the images, a triplet stream for calculating the distances and providing the constraint of the triplet loss, and a classification stream for ID level supervision provided by the classification-oriented loss, with the image triplets being generated by the proposed triplet sampling method. The  
Figure 10: Deep learning-based vehicle re-identification methods. deep CNN for feature extraction was fine-tuned from VGG CNN using the pre-trained weights from the ILSVRC-2012 dataset [10]. Stochastic gradient descent was employed during the training process. Results were gathered based on the VeRi dataset using the CMC and mAP, with model obtaining a mAP of 0.5740 on this dataset. While the results show a lower level of performance, making evident that simple triplet usage is not sufficient for robust ReID systems. Contrast loss is a useful tool for pair-wise marching, enabling the model to focus on discriminate features. A novel deep learning-based approach to progressive vehicle ReID, called PROVID, was proposed by [85]. The approach starts by addressing the ReID as two distinct search processes: coarse-to-fine search that operates in the feature space and near-to-distant search to address real-world scenarios. The first searching process employs the appearance attributes of the vehicle for coarse filtering, while simultaneously exploring Siamese Neural Network architecture for license plate verification for vehicle identification. The near-to-distant search process enables to retrieve vehicles' identity by searching from near to faraway cameras and from close to a distant time. To account for the spatial-temporal domain, an assumption is that two images have a higher probability of being the same object, if they have a small space or time distance among frames, and a lower probability of being the same vehicle if they have large space or time distance.