# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.3 Sequence Learning for ReID  
One common approach to capture the spatial-temporal cues for the task of ReID is to explore a sequence of videos or a small set of images to train RNN models that can be \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & **\begin{tabular}{} **\# Data** \\ **Success** \\ \end{tabular} & **Pros/Cons** \\ \hline \multirow{8}{*}{Contrast Loss} & \multirow{4}{*}{[43]} & Unsupervised, ResNet50 & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & Good dataset \\  & & features, Siamese networks, & CUHK03, & generalization, \\  & & Bayesian fusion & CMC 0.857 & Cross domains, \\  & & & Complex \\ \cline{2-4}  & \multirow{2}{*}{[46]} & Filter pairing neural & CHUK03, & Bad performance \\  & & network & CMC 0.206 & Complex, \\  & & & Not robust \\ \cline{2-4}  & \multirow{2}{*}{[50]} & Unsupervised, ResNet50 & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & Good dataset \\  & & features, Siamese networks, & CUHK03, & Generalization, \\  & & Bayesian fusion, & CMC 0.857 & Cross domains, \\  & & spatial–temporal model & Complex \\ \cline{2-4}  & \multirow{2}{*}{[52]} & Siamese networks, & CUHK03 & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & Tied convolution & CMC 0.547 & Simple, reusable \\ \hline \multirow{8}{*}{Triplet Loss} & \multirow{2}{*}{[53]} & Triplet loss, & CUHK03 & Simple to replicate, \\  & pre-trained ResNet & CMC 0.876 & architecture poses \\ \cline{2-4}  & \multirow{2}{*}{[60]} & Three CNN with shared & \multirow{2}{*}{i-LIDS} & Simple train \\  & weights, modified triplet & & & Scalable \\  & loss & & & Efficient \\ \hline \hline \end{tabular}  
* CMC—Cumulative matching characteristic (higher, the better), mAP—mean average precision (higher, the better), all measures range: [0.0, 1.0].  
\end{table} 
Table 3: Performance evaluation of the reviewed person ReID using deep metric methods. Figure 6: Performance summary of the evaluated deep learning metric methods. employed in the person ReID task. Many approaches explore 3D CNN to capture temporal and spatial features simultaneously [64]. The 3D CNN, in combination with a non-local attention mechanism, was proposed by [64] for person ReID, inspired by video action recognition models that involve the identification of different actions from video tracks. For the task, 3D convolutions on video volume, instead of using 2D convolutions across frames, are used to extract spatial and temporal features simultaneously. To handle misalignments, a non-local block is employed to capture spatial-temporal long-range dependencies, resulting in a network being able to learn useful spatial-temporal information as a weighted sum of the features in all space and temporal positions from the input feature map. Triplet loss function with hard mining proposed by [53] and a Softmax cross-entropy loss function with label smoothing regularization are employed to train the network. As for the network, 3D convolutions are replaced with two consecutive convolution layers, one one-dimensional (1D) convolution layer acting purely on the temporal axis, followed by a two-dimensional (2D) convolution layer to learn spatial features on the residual block. The modified 3D ResNet-50 is pre-trained on kinetics [65] to enhance the generalization performance of the model, and the final classification layer is replaced to output person identity. Experiments were performed using three datasets, namely, the iLIDS-VID, PRID-2011, and MARS datasets, and the results were compared with the ones of several state-of-the-art methods and of an established baseline model, which corresponds to a ResNet50 trained with Softmax cross-entropy loss and triplet with hard mining on an image-based person ReID. The proposed framework showed competitive results, outperforming several state-of-the-art approaches by a large margin on multiple metrics, attaining a mAP of 84.3% on the MARS dataset. In [66] is proposed a two-stream convolution network to extract spatial and temporal cues for video-based person ReID.