# 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
#### 6.2.2 Sequence Models for Vehicle ReID
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & \begin{tabular}{l} **\# Data** \\ **Success** \\ \end{tabular} & **Pros/Cons** \\ \hline \multirow{3}{*}{Triplet} & [82] & \begin{tabular}{l} CNN features, \\ group-sensitive-triplet emb. \\ \end{tabular} & \begin{tabular}{l} VehicleID \\ mAP 0.743 \\ \end{tabular} & \begin{tabular}{l} Reproducible, \\ ranking problems \\ \end{tabular} \\ \cline{2-5}  & [84] & \begin{tabular}{l} VGG features, \\ triplet sampling method \\ \end{tabular} & \begin{tabular}{l} VeRi-776 \\ mAP 0.574 \\ \end{tabular} & \begin{tabular}{l} reproducible, \\ robust \\ \end{tabular} \\ \hline \multirow{3}{*}{Contrast loss} & [85] & \begin{tabular}{l} Siamese Neural Net, \\ spatial–temporal similarity \\ \end{tabular} & \begin{tabular}{l} VeRi-776 \\ mAP 27.77 \\ \end{tabular} & \begin{tabular}{l} Simple, \\ not robust \\ \end{tabular} \\ \cline{1-1} \cline{2-5}  & [86] & \begin{tabular}{l} Siamese-RPN, switcher \\ -aware classification (SAC) \\ \end{tabular} & \
\begin{tabular}{l} MOT16 \\ CLEAR 0.712 \\ \end{tabular} &
\begin{tabular}{l} Complex, \\ trajectory ID handled \\ \end{tabular} \\ \hline \hline \end{tabular}  
mAP—mean average precision (higher, the better; range: \([0.0,100.0]\)).  
\end{table} 
Table 7: Performance evaluation of the reviewed vehicle ReID using deep leaning metric methods. through cosine similarity with the overall loss function containing the cluster loss, trajectory consistency loss, and classification loss. During the inference phase, a re-ranking with spatial-temporal cue is used, with all trajectories encoded in a feature vector of 2048 dimensions. A density clustering DBSCAN is used to gather similar vehicles with the different ID that belong to the same class. Finally, a ranking with weighted features and trajectory information is set in place to identify individual trajectories from the class group. Conducted experiments using the AI city challenge dataset achieved a mAP of 0.730, showing competitive tracking results in real urban scenarios. In [93] is proposed an extension to the prevalent task of multi-object tracking and segmentation (MOTS). It explores widely annotated dense pixel-level annotations of two existing tracking datasets using a semi-automatic annotation procedure, containing the masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames. To tackle detection, tracking, and segmentation, i.e., the MOTS task, a neural network is employed jointly with a baseline method built upon the famous Mask R-CNN [39] architecture, which extends the faster R-CNN [94] detector with a mask head. The TrackR-CNN model provides mask-based detection and association features. Both are used as input to a tracking algorithm that decides which detection to select, and how to integrate temporal context information. The temporal context of the input video is explored by the integration of 3D convolutions (to account for time), into Mask R-CNN on top of a ResNet-101 [14] backbone. The 3D convolutions layers are used to extract the backbone features and to obtain a temporal augmentation context. These new augmented features are then used by the Region Proposal Network (RPN) for the ReID task. For evaluation purposes, the proposed method was set as a baseline that jointly addresses detection, tracking, and segmentation with a single CNN. Conducted experiments demonstrated the relevance of the constructed datasets, enabling them to achieve considerable improvements in performance when trained on MOTS annotations. The datasets, proposed metrics, and baselines, such as MOTSA and sMOTSA, were considered with the baseline model achieving a sMOTSA of 52.7% and MOTSA of 66.9% on the KITTI MOTS dataset, enabling to account for temporal multi-object ReID. Table 8 summarizes the discussed works and establish comparisons among the performance and used datasets.