# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods
#### 5.2.1 Feature Learning
Attention mechanisms are also explored by [41], by introducing a bilateral complementary network (BiCnet), formed by a two-branch scheme; the first operating in the original image resolution, and the second called context branch, operating in downsampled resolution to capture long-range context. A specific attention mechanism, called diverse attention operation, was added to enforce consecutive frames to focus on different body characteristics regarding each identity. The mining of spatial clues was carried by a temporal kernel selection to jointly combine the short- and long-term temporal relations. Exhaustive experiments were conducted in the MARS, DukeMTMC-VideoReID, and LS-VID datasets, with the model obtaining a mAP of 0.860 on MARS dataset. Attention mechanisms used to obtain more robust salient features from images are proposed by [42]. Since complex backgrounds can generate salient features that can degrade the performance of the ReID task, a joint weak saliency, in combination with an aware attention mechanism, is set in place to obtain refined global features, while weakening some of the saliency features. Similar to [34], this model employs a ResNet scheme from the weekend saliency block, where an attention mechanism is set in place, and final results of both processes are fused together to form the final feature. The performance of the method is evaluated using the Market-1501 and DukeMTMC-ReID datasets, with the method achieving a mAP of 89.2% in the Market-1501 dataset. A performance evaluation of the reviewed methods for person ReID that explore Feature learning is presented in Table 2.  
A performance comparison among the described works is depicted in Figure 5. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Cat** & **Ref.** & **Main Technique(s)** & **\# Data Success** & **Pros/Cons** \\ \hline Fusion & [19] & KDE and CNN features, late fusion, SVM model & CAVIAR4 reid CMC 0.933 & Robust, simple \\ \cline{2-5}  & [24] & GOG and ResNet features, Data augmentation & CAVIAR4 reid CMC 0.919 & Simple, lack train data \\ \cline{2-5}  & [26] & Joint learning multi-loss, two-branch CNN & CHUK03, CMC 0.832 & Simple, efficient \\ \cline{2-5}  & [31] & Residual blocks, multi-scale feature & Market-1501 CMC 0.948 & Hard to train, can over fit \\ \cline{2-5}  & [34] & Weaken feature convolution, ResNet & Market-1501 mAP 0.942 & Robust, reusable \\ \hline Strip & [35] & Neural architecture, search (NAS) & Market-1501 CMC 0.954 & Hard to train, complex, not reusable \\ \hline Drop & [4] & Modified dropout layer & CUHK03 CMC 0.666 & Easy train, data domain, problematic \\ \hline \multirow{7}{*}{Attention} & [37] & ResNet-50 as feature extractor, attention mechanism & RAP 0.862 & Simple to replicate, reusable \\ \cline{2-5}  & [40] & ResNet-50 as feature extractor, multi-directional and level attention maps & CUHK03 CMC 0.918 & Complex, not reusable state-of-the-art \\ \cline{2-5}  & [41] & Two Branch, multi-scale and attention maps & MARS & Complex, reusable state-of-the-art \\ \cline{2-5}  & [42] & Attention, saliency maps ResNet & Market-1501 mAP 0.892 & Complex, generalizes well, state-of-the-art \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of the reviewed person ReID using feature learning methods. Although the Fusion scheme of local and global features is a reasonable approach to explore, the use of attention mechanisms enables leveraging the performance of person ReID, by capturing important aspects that are relevant to objectives set-in place.