# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 6 ReID and Spatial-Temporal Multi Object ReID Methods
### Multi Object ReID Datasets with Trajectories  
Multi-Object ReID considers attributes, such as shape and category combined with trajectories, and is one of the objectives that go towards the objective to perform multi-object  
Figure 8: Performance summary of the evaluated generative model methods.  
ReID in a real urban scenario. However, the development of suitable benchmark datasets for attribute recognition remains sparse. Some object ReID datasets contain trajectories collected from various sources, and such heterogeneous collection poses a significant challenge in developing high-quality fine-grained multi-object recognition algorithms.  
Among the publicly available datasets for ReID, one example is the NGSIM dataset [78], a publicly available data set with hand-coded Ground Truth (GT) that enables evaluating multi-camera, multi-vehicle tracking algorithms on real data, quantitatively. This dataset includes multiple views of a dense traffic scene with stop-and-go driving patterns, numerous partial and complete occlusions, and several intersections.  
Another example is the KITTI Vision Benchmark Suite [79], which is composed of several datasets for a wide range of tasks, such as stereo, optical flow, visual odometry, 3D object detection, and 3D tracking, complemented with accurate ground truth provided by Velodyne laser 3D scanner and real GPS localization system, Figure 9. The datasets were captured by driving around the mid-size city of Karlsruhe, in Germany, in rural areas, and on highways, and on average, there are up to 15 cars and 30 pedestrians per image. A detailed evaluation metric and evaluation site are also provided.  
In addition, the PASCAL VOC project [80], provides standardized image datasets for object class recognition, with annotations that enable evaluation and comparison of different methods. Another useful dataset is ImageNet [10]. This image database is organized according to the WordNet hierarchy (currently only the nouns), where each node is represented by hundreds and thousands of images. Currently, each node contains an average of over 500 images. The dataset encompasses a total of 21,841 non-empty sets, forming a total number of images of around 14 million, with several images with bounding box annotations of 1 million. In addition, it contains 1.2 million images with pre-computed SIFT features [81]. Pre-trained networks on these two datasets are commonly reused as backbones for feature extraction to perform several tasks, such as object ReID.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & \# & \# & \# & \multirow{2}{*}{**Label Method**} & \multirow{2}{*}{**Size**} & **Tracking Sequences** \\  & **Identities** & **Cameras** & & & & **Features** \\ \hline NGSIM & - & - & - & R-CNN & \(1392\times 512\) & Yes \\ KITTI & - & - & - & R-CNN & \(960\times 540\) & Yes \\ UA-DETRAC & 825 & 24 & 1.21 M & Manual & \(960\times 540\) & Yes \\ VehicleID & 26,267 & - & 221 K & Manual & Vary & Yes \\ VeRi-776 & 776 & 18 & 50 k & Manual & Vary & Yes \\ CompCar & 1687 & - & 18 k & Manual & Vary & Yes \\ PKU-Vehicle & - & - & 18 M & Manual & Cropped & Yes \\ MOT20-03 & 735 & - & 356 k & R-CNN & \(1173\times 880\) & Yes \\ MOT16 & - & - & 476 k & R-CNN & \(1920\times 1080\) & Yes \\ TRANCOS & 46,796 & - & 58 M & HOG & Vary & Yes \\ WebCamT & - & 212 & 60 k & - & - & No \\ \hline \hline \end{tabular} R-CNN—region proposals with CNN, HOG—histogram oriented gradients.  
\end{table}
Table 6: Global overview of the public available databases for multi-object car ReID with trajectories.  
Figure 9: Example of the multi-object tracking and segmentation system (MOTS).