# 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  
To capture temporal features, RNN are commonly employed. In [88], two end-to-end deep architectures, defined as the spatially concatenated CNN and CNN LSTM bi-directional loop, were proposed to address the problematic of vehicle viewpoint uncertainty. The models exploit the great advantages of CNN and LSTM to learn transformations across different viewpoints of vehicles, enabling to attain multi-view vehicle representation containing all viewpoints; information that can be inferred from the only one input view, and then used for learning to measure distance. The evaluation of the model was performed using a new proposed toy car ReID dataset with images from multiple viewpoints of 200 vehicles and the public multi-view car, VehicleID, and VeRi datasets. Conducted experiments showed that the proposed model could achieve a mAP of 18.13 on the VeRi dataset. In [89], a deep spatial-temporal neural network is proposed to solve the task of sequentially counting vehicles from low-quality videos acquired by city cameras (citycams). Citycam videos are characterized by low resolution, low frame rate, high occlusion, and broad perspective, making most existing methods lose efficacy. To overcome the limitations of the current methods and incorporate the temporal information of traffic video, a novel FCN-rLSTM network was proposed to jointly estimate vehicle density and vehicle count by connecting Fully Convolutional Network (FCN) with LSTM in a residual learning approach. The design enables leveraging the strengths of FCN for pixel-level prediction and the strengths of LSTM to learn complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as a learning residual function concerning the sum of densities in each frame, leading to a significant reduction in network training. To preserve feature map resolution, a hyper-atrous combination was proposed to integrate atrous convolution on the FCN, and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a new end-to-end trainable mapping from pixels to vehicle count. The proposed method was extensively evaluated on different counting tasks using three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, the proposed FCN-rLSTM reduced the Mean Absolute Error (MAE) from 5.31 to 4.21 on the TRANCOS dataset, showing the abilities of LSTM in combination with FCN to track objects in low-resolution videos. A practical vehicle tracking framework and trajectory-based weighted ranking method, which significantly improves the performance of cars ReID, were proposed in [90]. The proposed approach makes use of a ResNet50 [14] as the backbone for feature extraction, trained using the set of AI City Challenge [91] and VisDrone2018 [92] datasets, and only considering only the vehicle category. In the inference phase, the image is resized into \(1440\times 800\), to capture small vehicles in the video. By using the unified multi-object tracking framework proposed by [86], long-term and short-term cues are fully used as a detector with high recall, considering only boxes with higher confidence as input for the multiple target tracking algorithm. The similarity between the two features space is calculated