# 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
#### 6.2.4 Tracking for Vehicle ReID  
Continuously tracking of objects of interest is a common requirement for any ReID system that has been addressed using different strategies ranging form Kalman filtering based, Optical flow, and many combinations of those with other mechanism.  
In [102], the authors proposed an object tracking and 3D reconstruction method to perform 3D object motion estimation. Object tracking and 3D reconstruction are often performed together, with tracking used as input for the 3D reconstruction. To improve tracking performance, a novel method is proposed to close this gap, by first tracking and reconstructing to track. The proposed multi-object tracking, segmentation, and dynamic object fusion (MOTSFusion) approach exploits the 3D motion information extracted from dynamic object reconstructions to track objects through long periods with complete occlusion and recover missing detections. The method first builds up short tracklets using 2D optical flow and then fuses them into dynamic 3D object reconstructions. The precise 3D object motion of these reconstructions is used to merge tracklets through occlusion into long-term tracks and to locate objects in the absence of detection. Conducted experiments were performed on the KITTI platform [93; 103], with the reconstruction-based tracking reducing the number of ID switches of the initial tracklets by more than 50%. CLEARMOT [104] was adopted as evaluation metric for bounding box tracking to rank it in terms of MOTA [103], which incorporates FP, False Negative (FN), ID switches (IDS) and sMOTSA to account the segmentation Intersection over Union (IoU) accuracy, with the method achieving a MOTA of 84.83%. The method enables to robustly incorporate long-term occluded objects in an optimized manner.  
A recent and widely used tracking-by-detection algorithm is the DeepSort [105]. Simple online and real-time tracking (SORT) enables tracking multiple objects for more extended periods. The method relies on object detector backbones, such as Yolo [106], where an appearance integration is integrated into the Kalman filter to effectively track uniquely similar objects in strong occluded environments, while reducing the number of tracking or ID switch. When an object in track by the Kalman filter, the update of the filter is performed considering the dynamics, the association mechanism based on the Mahalanobis distance, with an extra feature concerning the object appearance, for each object bounding box appearance, a gallery of associated appearance descriptors is kept for each track. Cosine similarity on new observation is used to compute the distance of the current object in track and the new observation, enabling to correctly associate the observation even for an object with high dynamics that cannot be handled solely by the Kalman filter association metrics. The experimental evaluation shows that the inclusion of deep features into the Kalman filter reduces the number of identity switches by 45%.  
Table 10 summarizes the discussed works and establish comparisons among the performance and used datasets.  
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