# 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
#### 6.2.3 Feature Learning for Vehicle ReID  
Feature learning are sometimes implicit when using pre-trained backbones, such as ResNets. However, many works explore the use of specialized schemes to explore the potentialities of global and local features, often by using fusion schemes. In [95], the authors proposed an approach on vehicle ReID without any knowledge about localization or movements of the cars. This method obtains real-time traffic information based on linear regression with SVM, according to feature vectors, which consist of a color histogram and oriented gradients. First, the vehicles are detected in the video by an object classifier model that creates 3D bounding boxes around the cars. Only the side \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & **\# Data** & **Process/Cons** \\ \hline \multirow{4}{*}{LSTM} & [88] & Spatially Concatenated CNN, & VeRi-776 & Simple, \\  & CNN-LSTM bi-directional loop & mAP 18.13 & applicable \\ \cline{2-5}  & [89] & CNN features, gFCN- & TRANCOS & Reproducible, \\  & & rLSTM network + Atrous & MAE 4.21 & ranking problems \\  & [90] & ResNet50, LSTM & AI City & Complex, \\  & & + clustering DBSCAN & MAE 0.730 & trajectory problems \\ \hline \multirow{2}{*}{3D} & [93] & Mask R-CNN, & KITTI & Robust, \\  & & 3D convolutional layers & MOTS 0.669 & Short term ID handled \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance evaluation of the reviewed vehicle ReID using sequence learning methods. and front (or back) faces vehicle images are extracted. The extracted image is then fitted into a grid and color histograms to be found in another vehicle image set by simulating a different camera view to be used for the first-round regression. Vehicles with positive first-round regression results are then tested on the second-round regression, where the average Histogram of Oriented Gradients (HOG) vector is used. Cars with both regression results positive are added to another set and are considered as highly potential positive ReID candidates. Experiments were performed on pre-selected semi-automatically 1232 image pairs likely to be matching vehicles, and using a web interface and crowd-sourced people's opinion of vehicles that \"are likely to be the same vehicle\". The findings showed that 60% of matches could be retrieved (TPR), with only about 10% of False Positive (FP) being included. The proposed method lacks robustness and relies on great percentages on non-robust features that are not optimal to be used in a variety of urban scenarios. A two-branch CNN scheme was presented in [96] to learn deep features and the distance metric simultaneously. The proposed model uses the late fusion scheme to combine attributes and color features (FACT). It is the late fusion scheme that starts by ranking scores of all test images with the semantic feature learned by GoogLeNet [97] separately. Conducted experiments were performed against other methods, with the rank scores being calculated by the Euclidean distance. The model evaluation was performed on the VeRi dataset, with the proposed model achieving a mAP of 19.92. In [98], the authors proposed a new spatially constrained similarity measure (SCSM) to handle object rotation, scaling, viewpoint change, and appearance deformation for object ReID in combination with a robust re-ranking method with the k-nearest neighbors of a given query for automatically refining the initial search results. The retrieval system is implemented with SIFT descriptors [81] and fast approximate k-means clustering [99] creating a bag of words (BoW) classification scheme. Extensive performance evaluations on INRIA dataset achieves a mAP of 0.762. In [100], the authors presented a forecasting mechanism to forecast pedestrian destinations in a large area with a limited number of observations.